mirror of
https://github.com/gsi-upm/soil
synced 2024-10-31 17:11:42 +00:00
1913 lines
252 KiB
Plaintext
1913 lines
252 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-02T16:44:14.120953Z",
|
||
"start_time": "2017-07-02T18:44:14.117152+02:00"
|
||
}
|
||
},
|
||
"source": [
|
||
"# Introduction"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"cell_style": "center",
|
||
"collapsed": true
|
||
},
|
||
"source": [
|
||
"This notebook is an introduction to the soil agent-based social network simulation framework.\n",
|
||
"In particular, we will focus on a specific use case: studying the propagation of news in a social network.\n",
|
||
"\n",
|
||
"The steps we will follow are:\n",
|
||
"\n",
|
||
"* Modelling the behavior of agents\n",
|
||
"* Running the simulation using different configurations\n",
|
||
"* Analysing the results of each simulation"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T13:38:48.052876Z",
|
||
"start_time": "2017-07-03T15:38:48.044762+02:00"
|
||
}
|
||
},
|
||
"source": [
|
||
"But before that, let's import the soil module and networkx."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T14:42:51.679937Z",
|
||
"start_time": "2017-07-03T16:42:51.185463+02:00"
|
||
},
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import soil\n",
|
||
"import networkx as nx"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T14:42:51.690373Z",
|
||
"start_time": "2017-07-03T16:42:51.682644+02:00"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"%pylab inline\n",
|
||
"# To display plots in the notebook"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T13:41:19.788717Z",
|
||
"start_time": "2017-07-03T15:41:19.785448+02:00"
|
||
}
|
||
},
|
||
"source": [
|
||
"# Basic concepts"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"There are three main elements in a soil simulation:\n",
|
||
" \n",
|
||
"* The network topology. A simulation may use an existing NetworkX topology, or generate one on the fly\n",
|
||
"* Agents. There are two types: 1) network agents, which are linked to a node in the topology, and 2) environment agents, which are freely assigned to the environment.\n",
|
||
"* The environment. It assigns agents to nodes in the network, and stores the environment parameters (shared state for all agents).\n",
|
||
"\n",
|
||
"Soil is based on ``simpy``, which is an event-based network simulation library.\n",
|
||
"Soil provides several abstractions over events to make developing agents easier.\n",
|
||
"This means you can use events (timeouts, delays) in soil, but for the most part we will assume your models will be step-based.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-02T15:55:12.933978Z",
|
||
"start_time": "2017-07-02T17:55:12.930860+02:00"
|
||
}
|
||
},
|
||
"source": [
|
||
"# Modeling behaviour"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T13:49:31.269687Z",
|
||
"start_time": "2017-07-03T15:49:31.257850+02:00"
|
||
}
|
||
},
|
||
"source": [
|
||
"Our first step will be to model how every person in the social network reacts when it comes to news.\n",
|
||
"We will follow a very simple model (a finite state machine).\n",
|
||
"\n",
|
||
"There are two types of people, those who have heard about a newsworthy event (infected) or those who have not (neutral).\n",
|
||
"A neutral person may heard about the news either on the TV (with probability **prob_tv_spread**) or through their friends.\n",
|
||
"Once a person has heard the news, they will spread it to their friends (with a probability **prob_neighbor_spread**).\n",
|
||
"Some users do not have a TV, so they only rely on their friends.\n",
|
||
"\n",
|
||
"The spreading probabilities will change over time due to different factors.\n",
|
||
"We will represent this variance using an environment agent."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Network Agents"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T14:03:07.171127Z",
|
||
"start_time": "2017-07-03T16:03:07.165779+02:00"
|
||
}
|
||
},
|
||
"source": [
|
||
"A basic network agent in Soil should inherit from ``soil.agents.BaseAgent``, and define its behaviour in every step of the simulation by implementing a ``run(self)`` method.\n",
|
||
"The most important attributes of the agent are:\n",
|
||
"\n",
|
||
"* ``agent.state``, a dictionary with the state of the agent. ``agent.state['id']`` reflects the state id of the agent. That state id can be used to look for other networks in that specific state. The state can be access via the agent as well. For instance:\n",
|
||
"```py\n",
|
||
"a = soil.agents.BaseAgent(env=env)\n",
|
||
"a['hours_of_sleep'] = 10\n",
|
||
"print(a['hours_of_sleep'])\n",
|
||
"```\n",
|
||
" The state of the agent is stored in every step of the simulation:\n",
|
||
" ```py\n",
|
||
" print(a['hours_of_sleep', 10]) # hours of sleep before step #10\n",
|
||
" print(a[None, 0]) # whole state of the agent before step #0\n",
|
||
" ```\n",
|
||
"\n",
|
||
"* ``agent.env``, a reference to the environment. Most commonly used to get access to the environment parameters and the topology:\n",
|
||
" ```py\n",
|
||
" a.env.G.nodes() # Get all nodes ids in the topology\n",
|
||
" a.env['minimum_hours_of_sleep']\n",
|
||
"\n",
|
||
" ```\n",
|
||
"\n",
|
||
"Since our model is a finite state machine, we will be basing it on ``soil.agents.FSM``.\n",
|
||
"\n",
|
||
"With ``soil.agents.FSM``, we do not need to specify a ``step`` method.\n",
|
||
"Instead, we describe every step as a function.\n",
|
||
"To change to another state, a function may return the new state.\n",
|
||
"If no state is returned, the state remains unchanged.[\n",
|
||
"It will consist of two states, ``neutral`` (default) and ``infected``.\n",
|
||
"\n",
|
||
"Here's the code:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T14:42:51.715535Z",
|
||
"start_time": "2017-07-03T16:42:51.692301+02:00"
|
||
},
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import random\n",
|
||
"\n",
|
||
"class NewsSpread(soil.agents.FSM):\n",
|
||
" @soil.agents.default_state\n",
|
||
" @soil.agents.state\n",
|
||
" def neutral(self):\n",
|
||
" r = random.random()\n",
|
||
" if self['has_tv'] and r < self.env['prob_tv_spread']:\n",
|
||
" return self.infected\n",
|
||
" return\n",
|
||
" \n",
|
||
" @soil.agents.state\n",
|
||
" def infected(self):\n",
|
||
" prob_infect = self.env['prob_neighbor_spread']\n",
|
||
" for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):\n",
|
||
" r = random.random()\n",
|
||
" if r < prob_infect:\n",
|
||
" neighbor.state['id'] = self.infected.id\n",
|
||
" return\n",
|
||
" "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-02T12:22:53.931963Z",
|
||
"start_time": "2017-07-02T14:22:53.928340+02:00"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Environment agents"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Environment agents allow us to control the state of the environment.\n",
|
||
"In this case, we will use an environment agent to simulate a very viral event.\n",
|
||
"\n",
|
||
"When the event happens, the agent will modify the probability of spreading the rumor."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T14:42:51.727938Z",
|
||
"start_time": "2017-07-03T16:42:51.717828+02:00"
|
||
},
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"NEIGHBOR_FACTOR = 0.9\n",
|
||
"TV_FACTOR = 0.5\n",
|
||
"class NewsEnvironmentAgent(soil.agents.BaseAgent):\n",
|
||
" def step(self):\n",
|
||
" if self.now == self['event_time']:\n",
|
||
" self.env['prob_tv_spread'] = 1\n",
|
||
" self.env['prob_neighbor_spread'] = 1\n",
|
||
" elif self.now > self['event_time']:\n",
|
||
" self.env['prob_tv_spread'] = self.env['prob_tv_spread'] * TV_FACTOR\n",
|
||
" self.env['prob_neighbor_spread'] = self.env['prob_neighbor_spread'] * NEIGHBOR_FACTOR"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-02T11:23:18.052235Z",
|
||
"start_time": "2017-07-02T13:23:18.047452+02:00"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Testing the agents"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-02T16:14:54.572431Z",
|
||
"start_time": "2017-07-02T18:14:54.564095+02:00"
|
||
}
|
||
},
|
||
"source": [
|
||
"Feel free to skip this section if this is your first time with soil."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Testing agents is not easy, and this is not a thorough testing process for agents.\n",
|
||
"Rather, this section is aimed to show you how to access internal pats of soil so you can test your agents."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"cell_style": "split"
|
||
},
|
||
"source": [
|
||
"First of all, let's check if our network agent has the states we would expect:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T14:42:51.816465Z",
|
||
"start_time": "2017-07-03T16:42:51.811222+02:00"
|
||
},
|
||
"cell_style": "split"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'infected': <function __main__.NewsSpread.infected>,\n",
|
||
" 'neutral': <function __main__.NewsSpread.neutral>}"
|
||
]
|
||
},
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"NewsSpread.states"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"cell_style": "split"
|
||
},
|
||
"source": [
|
||
"Now, let's run a simulation on a simple network. It is comprised of three nodes:\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T14:42:52.106636Z",
|
||
"start_time": "2017-07-03T16:42:51.904738+02:00"
|
||
},
|
||
"cell_style": "split",
|
||
"scrolled": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/X8ieN7dQkZLXwAAAABJRU5ErkJggg==\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7fd08276d978>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"G = nx.Graph()\n",
|
||
"G.add_edge(0, 1)\n",
|
||
"G.add_edge(0, 2)\n",
|
||
"G.add_edge(2, 3)\n",
|
||
"G.add_node(4)\n",
|
||
"pos = nx.spring_layout(G)\n",
|
||
"nx.draw_networkx(G, pos, node_color='red')\n",
|
||
"nx.draw_networkx(G, pos, nodelist=[0], node_color='blue')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T11:53:30.997756Z",
|
||
"start_time": "2017-07-03T13:53:30.989609+02:00"
|
||
},
|
||
"cell_style": "split"
|
||
},
|
||
"source": [
|
||
"Let's run a simple simulation that assigns a NewsSpread agent to all the nodes in that network.\n",
|
||
"Notice how node 0 is the only one with a TV."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T14:42:52.136477Z",
|
||
"start_time": "2017-07-03T16:42:52.108729+02:00"
|
||
},
|
||
"cell_style": "split"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Trial: 0\n",
|
||
"\tRunning\n",
|
||
"Finished trial in 0.014928102493286133 seconds\n",
|
||
"Finished simulation in 0.015764951705932617 seconds\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"env_params = {'prob_tv_spread': 0,\n",
|
||
" 'prob_neighbor_spread': 0}\n",
|
||
"\n",
|
||
"MAX_TIME = 100\n",
|
||
"EVENT_TIME = 10\n",
|
||
"\n",
|
||
"sim = soil.simulation.SoilSimulation(topology=G,\n",
|
||
" num_trials=1,\n",
|
||
" max_time=MAX_TIME,\n",
|
||
" environment_agents=[{'agent_type': NewsEnvironmentAgent,\n",
|
||
" 'state': {\n",
|
||
" 'event_time': EVENT_TIME\n",
|
||
" }}],\n",
|
||
" network_agents=[{'agent_type': NewsSpread,\n",
|
||
" 'weight': 1}],\n",
|
||
" states={0: {'has_tv': True}},\n",
|
||
" default_state={'has_tv': False},\n",
|
||
" environment_params=env_params)\n",
|
||
"env = sim.run_simulation()[0]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"cell_style": "split"
|
||
},
|
||
"source": [
|
||
"Now we can access the results of the simulation and compare them to our expected results"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T14:42:52.160856Z",
|
||
"start_time": "2017-07-03T16:42:52.138976+02:00"
|
||
},
|
||
"cell_style": "split",
|
||
"collapsed": true,
|
||
"scrolled": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"agents = list(env.network_agents)\n",
|
||
"\n",
|
||
"# Until the event, all agents are neutral\n",
|
||
"for t in range(10):\n",
|
||
" for a in agents:\n",
|
||
" assert a['id', t] == a.neutral.id\n",
|
||
"\n",
|
||
"# After the event, the node with a TV is infected, the rest are not\n",
|
||
"assert agents[0]['id', 11] == NewsSpread.infected.id\n",
|
||
"\n",
|
||
"for a in agents[1:4]:\n",
|
||
" assert a['id', 11] == NewsSpread.neutral.id\n",
|
||
"\n",
|
||
"# At the end, the agents connected to the infected one will probably be infected, too.\n",
|
||
"assert agents[1]['id', MAX_TIME] == NewsSpread.infected.id\n",
|
||
"assert agents[2]['id', MAX_TIME] == NewsSpread.infected.id\n",
|
||
"\n",
|
||
"# But the node with no friends should not be affected\n",
|
||
"assert agents[4]['id', MAX_TIME] == NewsSpread.neutral.id\n",
|
||
" "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-02T16:41:09.110652Z",
|
||
"start_time": "2017-07-02T18:41:09.106966+02:00"
|
||
},
|
||
"cell_style": "split"
|
||
},
|
||
"source": [
|
||
"Lastly, let's see if the probabilities have decreased as expected:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T14:42:52.193918Z",
|
||
"start_time": "2017-07-03T16:42:52.163476+02:00"
|
||
},
|
||
"cell_style": "split",
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"assert abs(env.environment_params['prob_neighbor_spread'] - (NEIGHBOR_FACTOR**(MAX_TIME-1-10))) < 10e-4\n",
|
||
"assert abs(env.environment_params['prob_tv_spread'] - (TV_FACTOR**(MAX_TIME-1-10))) < 10e-6"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Running the simulation"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T11:20:28.566944Z",
|
||
"start_time": "2017-07-03T13:20:28.561052+02:00"
|
||
},
|
||
"cell_style": "split"
|
||
},
|
||
"source": [
|
||
"To run a simulation, we need a configuration.\n",
|
||
"Soil can load configurations from python dictionaries as well as JSON and YAML files.\n",
|
||
"For this demo, we will use a python dictionary:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T14:42:52.219072Z",
|
||
"start_time": "2017-07-03T16:42:52.196203+02:00"
|
||
},
|
||
"cell_style": "split",
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"config = {\n",
|
||
" 'name': 'ExampleSimulation',\n",
|
||
" 'max_time': 20,\n",
|
||
" 'interval': 1,\n",
|
||
" 'num_trials': 1,\n",
|
||
" 'network_params': {\n",
|
||
" 'generator': 'complete_graph',\n",
|
||
" 'n': 500,\n",
|
||
" },\n",
|
||
" 'network_agents': [\n",
|
||
" {\n",
|
||
" 'agent_type': NewsSpread,\n",
|
||
" 'weight': 1,\n",
|
||
" 'state': {\n",
|
||
" 'has_tv': False\n",
|
||
" }\n",
|
||
" },\n",
|
||
" {\n",
|
||
" 'agent_type': NewsSpread,\n",
|
||
" 'weight': 2,\n",
|
||
" 'state': {\n",
|
||
" 'has_tv': True\n",
|
||
" }\n",
|
||
" }\n",
|
||
" ],\n",
|
||
" 'states': [ {'has_tv': True} ],\n",
|
||
" 'environment_params':{\n",
|
||
" 'prob_tv_spread': 0.01,\n",
|
||
" 'prob_neighbor_spread': 0.5\n",
|
||
" }\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T11:57:34.219618Z",
|
||
"start_time": "2017-07-03T13:57:34.213817+02:00"
|
||
},
|
||
"cell_style": "split"
|
||
},
|
||
"source": [
|
||
"Let's run our simulation:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T14:42:55.366288Z",
|
||
"start_time": "2017-07-03T16:42:52.295584+02:00"
|
||
},
|
||
"cell_style": "split"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Using config(s): ExampleSimulation\n",
|
||
"Trial: 0\n",
|
||
"\tRunning\n",
|
||
"Finished trial in 1.4140360355377197 seconds\n",
|
||
"Finished simulation in 2.4056642055511475 seconds\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"soil.simulation.run_from_config(config, dump=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T12:03:32.183588Z",
|
||
"start_time": "2017-07-03T14:03:32.167797+02:00"
|
||
},
|
||
"cell_style": "split",
|
||
"collapsed": true
|
||
},
|
||
"source": [
|
||
"In real life, you probably want to run several simulations, varying some of the parameters so that you can compare and answer your research questions.\n",
|
||
"\n",
|
||
"For instance:\n",
|
||
" \n",
|
||
"* Does the outcome depend on the structure of our network? We will use different generation algorithms to compare them (Barabasi-Albert and Erdos-Renyi)\n",
|
||
"* How does neighbor spreading probability affect my simulation? We will try probability values in the range of [0, 0.4], in intervals of 0.1."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T14:43:15.488799Z",
|
||
"start_time": "2017-07-03T16:42:55.368021+02:00"
|
||
},
|
||
"cell_style": "split",
|
||
"scrolled": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Using config(s): Spread_erdos_renyi_graph_prob_0.0\n",
|
||
"Trial: 0\n",
|
||
"\tRunning\n",
|
||
"Finished trial in 0.2691483497619629 seconds\n",
|
||
"Finished simulation in 0.3650345802307129 seconds\n",
|
||
"Using config(s): Spread_erdos_renyi_graph_prob_0.1\n",
|
||
"Trial: 0\n",
|
||
"\tRunning\n",
|
||
"Finished trial in 0.34261059761047363 seconds\n",
|
||
"Finished simulation in 0.44017767906188965 seconds\n",
|
||
"Using config(s): Spread_erdos_renyi_graph_prob_0.2\n",
|
||
"Trial: 0\n",
|
||
"\tRunning\n",
|
||
"Finished trial in 0.34417223930358887 seconds\n",
|
||
"Finished simulation in 0.4550771713256836 seconds\n",
|
||
"Using config(s): Spread_erdos_renyi_graph_prob_0.3\n",
|
||
"Trial: 0\n",
|
||
"\tRunning\n",
|
||
"Finished trial in 0.3237779140472412 seconds\n",
|
||
"Finished simulation in 0.42307496070861816 seconds\n",
|
||
"Using config(s): Spread_erdos_renyi_graph_prob_0.4\n",
|
||
"Trial: 0\n",
|
||
"\tRunning\n",
|
||
"Finished trial in 0.3507683277130127 seconds\n",
|
||
"Finished simulation in 0.45061564445495605 seconds\n",
|
||
"Using config(s): Spread_barabasi_albert_graph_prob_0.0\n",
|
||
"Trial: 0\n",
|
||
"\tRunning\n",
|
||
"Finished trial in 0.19115304946899414 seconds\n",
|
||
"Finished simulation in 0.20927715301513672 seconds\n",
|
||
"Using config(s): Spread_barabasi_albert_graph_prob_0.1\n",
|
||
"Trial: 0\n",
|
||
"\tRunning\n",
|
||
"Finished trial in 0.22086191177368164 seconds\n",
|
||
"Finished simulation in 0.2390913963317871 seconds\n",
|
||
"Using config(s): Spread_barabasi_albert_graph_prob_0.2\n",
|
||
"Trial: 0\n",
|
||
"\tRunning\n",
|
||
"Finished trial in 0.21225976943969727 seconds\n",
|
||
"Finished simulation in 0.23252630233764648 seconds\n",
|
||
"Using config(s): Spread_barabasi_albert_graph_prob_0.3\n",
|
||
"Trial: 0\n",
|
||
"\tRunning\n",
|
||
"Finished trial in 0.2853121757507324 seconds\n",
|
||
"Finished simulation in 0.30568504333496094 seconds\n",
|
||
"Using config(s): Spread_barabasi_albert_graph_prob_0.4\n",
|
||
"Trial: 0\n",
|
||
"\tRunning\n",
|
||
"Finished trial in 0.21434736251831055 seconds\n",
|
||
"Finished simulation in 0.23370599746704102 seconds\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"network_1 = {\n",
|
||
" 'generator': 'erdos_renyi_graph',\n",
|
||
" 'n': 500,\n",
|
||
" 'p': 0.1\n",
|
||
"}\n",
|
||
"network_2 = {\n",
|
||
" 'generator': 'barabasi_albert_graph',\n",
|
||
" 'n': 500,\n",
|
||
" 'm': 2\n",
|
||
"}\n",
|
||
"\n",
|
||
"\n",
|
||
"for net in [network_1, network_2]:\n",
|
||
" for i in range(5):\n",
|
||
" prob = i / 10\n",
|
||
" config['environment_params']['prob_neighbor_spread'] = prob\n",
|
||
" config['network_params'] = net\n",
|
||
" config['name'] = 'Spread_{}_prob_{}'.format(net['generator'], prob)\n",
|
||
" s = soil.simulation.run_from_config(config)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T11:05:18.043194Z",
|
||
"start_time": "2017-07-03T13:05:18.034699+02:00"
|
||
},
|
||
"cell_style": "split"
|
||
},
|
||
"source": [
|
||
"The results are conveniently stored in pickle (simulation), csv (history of agent and environment state) and gexf format."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T14:43:15.721720Z",
|
||
"start_time": "2017-07-03T16:43:15.490854+02:00"
|
||
},
|
||
"cell_style": "split",
|
||
"scrolled": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\u001b[01;34msoil_output\u001b[00m\n",
|
||
"├── \u001b[01;34mSim_prob_0\u001b[00m\n",
|
||
"│ ├── Sim_prob_0.dumped.yml\n",
|
||
"│ ├── Sim_prob_0.simulation.pickle\n",
|
||
"│ ├── Sim_prob_0_trial_0.environment.csv\n",
|
||
"│ └── Sim_prob_0_trial_0.gexf\n",
|
||
"├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.0\u001b[00m\n",
|
||
"│ ├── Spread_barabasi_albert_graph_prob_0.0.dumped.yml\n",
|
||
"│ ├── Spread_barabasi_albert_graph_prob_0.0.simulation.pickle\n",
|
||
"│ ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.environment.csv\n",
|
||
"│ └── Spread_barabasi_albert_graph_prob_0.0_trial_0.gexf\n",
|
||
"├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.1\u001b[00m\n",
|
||
"│ ├── Spread_barabasi_albert_graph_prob_0.1.dumped.yml\n",
|
||
"│ ├── Spread_barabasi_albert_graph_prob_0.1.simulation.pickle\n",
|
||
"│ ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.environment.csv\n",
|
||
"│ └── Spread_barabasi_albert_graph_prob_0.1_trial_0.gexf\n",
|
||
"├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.2\u001b[00m\n",
|
||
"│ ├── Spread_barabasi_albert_graph_prob_0.2.dumped.yml\n",
|
||
"│ ├── Spread_barabasi_albert_graph_prob_0.2.simulation.pickle\n",
|
||
"│ ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.environment.csv\n",
|
||
"│ └── Spread_barabasi_albert_graph_prob_0.2_trial_0.gexf\n",
|
||
"├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.3\u001b[00m\n",
|
||
"│ ├── Spread_barabasi_albert_graph_prob_0.3.dumped.yml\n",
|
||
"│ ├── Spread_barabasi_albert_graph_prob_0.3.simulation.pickle\n",
|
||
"│ ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.environment.csv\n",
|
||
"│ └── Spread_barabasi_albert_graph_prob_0.3_trial_0.gexf\n",
|
||
"├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.4\u001b[00m\n",
|
||
"│ ├── Spread_barabasi_albert_graph_prob_0.4.dumped.yml\n",
|
||
"│ ├── Spread_barabasi_albert_graph_prob_0.4.simulation.pickle\n",
|
||
"│ ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.environment.csv\n",
|
||
"│ └── Spread_barabasi_albert_graph_prob_0.4_trial_0.gexf\n",
|
||
"├── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.0\u001b[00m\n",
|
||
"│ ├── Spread_erdos_renyi_graph_prob_0.0.dumped.yml\n",
|
||
"│ ├── Spread_erdos_renyi_graph_prob_0.0.simulation.pickle\n",
|
||
"│ ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.environment.csv\n",
|
||
"│ └── Spread_erdos_renyi_graph_prob_0.0_trial_0.gexf\n",
|
||
"├── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.1\u001b[00m\n",
|
||
"│ ├── Spread_erdos_renyi_graph_prob_0.1.dumped.yml\n",
|
||
"│ ├── Spread_erdos_renyi_graph_prob_0.1.simulation.pickle\n",
|
||
"│ ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.environment.csv\n",
|
||
"│ └── Spread_erdos_renyi_graph_prob_0.1_trial_0.gexf\n",
|
||
"├── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.2\u001b[00m\n",
|
||
"│ ├── Spread_erdos_renyi_graph_prob_0.2.dumped.yml\n",
|
||
"│ ├── Spread_erdos_renyi_graph_prob_0.2.simulation.pickle\n",
|
||
"│ ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.environment.csv\n",
|
||
"│ └── Spread_erdos_renyi_graph_prob_0.2_trial_0.gexf\n",
|
||
"├── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.3\u001b[00m\n",
|
||
"│ ├── Spread_erdos_renyi_graph_prob_0.3.dumped.yml\n",
|
||
"│ ├── Spread_erdos_renyi_graph_prob_0.3.simulation.pickle\n",
|
||
"│ ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.environment.csv\n",
|
||
"│ └── Spread_erdos_renyi_graph_prob_0.3_trial_0.gexf\n",
|
||
"└── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.4\u001b[00m\n",
|
||
" ├── Spread_erdos_renyi_graph_prob_0.4.dumped.yml\n",
|
||
" ├── Spread_erdos_renyi_graph_prob_0.4.simulation.pickle\n",
|
||
" ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.environment.csv\n",
|
||
" └── Spread_erdos_renyi_graph_prob_0.4_trial_0.gexf\n",
|
||
"\n",
|
||
"11 directories, 44 files\n",
|
||
"1.8M\tsoil_output/Sim_prob_0\n",
|
||
"652K\tsoil_output/Spread_barabasi_albert_graph_prob_0.0\n",
|
||
"684K\tsoil_output/Spread_barabasi_albert_graph_prob_0.1\n",
|
||
"692K\tsoil_output/Spread_barabasi_albert_graph_prob_0.2\n",
|
||
"692K\tsoil_output/Spread_barabasi_albert_graph_prob_0.3\n",
|
||
"688K\tsoil_output/Spread_barabasi_albert_graph_prob_0.4\n",
|
||
"1.8M\tsoil_output/Spread_erdos_renyi_graph_prob_0.0\n",
|
||
"1.9M\tsoil_output/Spread_erdos_renyi_graph_prob_0.1\n",
|
||
"1.9M\tsoil_output/Spread_erdos_renyi_graph_prob_0.2\n",
|
||
"1.9M\tsoil_output/Spread_erdos_renyi_graph_prob_0.3\n",
|
||
"1.9M\tsoil_output/Spread_erdos_renyi_graph_prob_0.4\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"!tree soil_output\n",
|
||
"!du -xh soil_output/*"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-02T10:40:14.384177Z",
|
||
"start_time": "2017-07-02T12:40:14.381885+02:00"
|
||
}
|
||
},
|
||
"source": [
|
||
"# Analysing the results"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Once the simulations are over, we can use soil to analyse the results."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T14:44:30.978223Z",
|
||
"start_time": "2017-07-03T16:44:30.971952+02:00"
|
||
}
|
||
},
|
||
"source": [
|
||
"First, let's load the stored results into a pandas dataframe."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T14:43:15.987794Z",
|
||
"start_time": "2017-07-03T16:43:15.724519+02:00"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/usr/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['random']\n",
|
||
"`%matplotlib` prevents importing * from pylab and numpy\n",
|
||
" \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"%pylab inline\n",
|
||
"from soil import analysis"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T14:43:16.590910Z",
|
||
"start_time": "2017-07-03T16:43:15.990320+02:00"
|
||
},
|
||
"scrolled": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style>\n",
|
||
" .dataframe thead tr:only-child th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: left;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>agent_id</th>\n",
|
||
" <th>tstep</th>\n",
|
||
" <th>attribute</th>\n",
|
||
" <th>value</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>prob_tv_spread</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>prob_neighbor_spread</td>\n",
|
||
" <td>0.1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>prob_tv_spread</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>prob_neighbor_spread</td>\n",
|
||
" <td>0.1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>prob_tv_spread</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>prob_neighbor_spread</td>\n",
|
||
" <td>0.1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>prob_tv_spread</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>prob_neighbor_spread</td>\n",
|
||
" <td>0.1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>prob_tv_spread</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>prob_neighbor_spread</td>\n",
|
||
" <td>0.1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>prob_tv_spread</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>prob_neighbor_spread</td>\n",
|
||
" <td>0.1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>prob_tv_spread</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>prob_neighbor_spread</td>\n",
|
||
" <td>0.1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>7</td>\n",
|
||
" <td>prob_tv_spread</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>7</td>\n",
|
||
" <td>prob_neighbor_spread</td>\n",
|
||
" <td>0.1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>prob_tv_spread</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>prob_neighbor_spread</td>\n",
|
||
" <td>0.1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>18</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>9</td>\n",
|
||
" <td>prob_tv_spread</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>19</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>9</td>\n",
|
||
" <td>prob_neighbor_spread</td>\n",
|
||
" <td>0.1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>20</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>prob_tv_spread</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>prob_neighbor_spread</td>\n",
|
||
" <td>0.1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>22</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>11</td>\n",
|
||
" <td>prob_tv_spread</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>23</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>11</td>\n",
|
||
" <td>prob_neighbor_spread</td>\n",
|
||
" <td>0.1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>24</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>12</td>\n",
|
||
" <td>prob_tv_spread</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>12</td>\n",
|
||
" <td>prob_neighbor_spread</td>\n",
|
||
" <td>0.1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>26</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>13</td>\n",
|
||
" <td>prob_tv_spread</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>27</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>13</td>\n",
|
||
" <td>prob_neighbor_spread</td>\n",
|
||
" <td>0.1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>28</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>14</td>\n",
|
||
" <td>prob_tv_spread</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>29</th>\n",
|
||
" <td>env</td>\n",
|
||
" <td>14</td>\n",
|
||
" <td>prob_neighbor_spread</td>\n",
|
||
" <td>0.1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21012</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>has_tv</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21013</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>id</td>\n",
|
||
" <td>neutral</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21014</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>7</td>\n",
|
||
" <td>has_tv</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21015</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>7</td>\n",
|
||
" <td>id</td>\n",
|
||
" <td>neutral</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21016</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>has_tv</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21017</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>id</td>\n",
|
||
" <td>neutral</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21018</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>9</td>\n",
|
||
" <td>has_tv</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21019</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>9</td>\n",
|
||
" <td>id</td>\n",
|
||
" <td>neutral</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21020</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>has_tv</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21021</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>id</td>\n",
|
||
" <td>neutral</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21022</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>11</td>\n",
|
||
" <td>has_tv</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21023</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>11</td>\n",
|
||
" <td>id</td>\n",
|
||
" <td>neutral</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21024</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>12</td>\n",
|
||
" <td>has_tv</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21025</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>12</td>\n",
|
||
" <td>id</td>\n",
|
||
" <td>neutral</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21026</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>13</td>\n",
|
||
" <td>has_tv</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21027</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>13</td>\n",
|
||
" <td>id</td>\n",
|
||
" <td>neutral</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21028</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>14</td>\n",
|
||
" <td>has_tv</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21029</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>14</td>\n",
|
||
" <td>id</td>\n",
|
||
" <td>neutral</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21030</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>has_tv</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21031</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>id</td>\n",
|
||
" <td>neutral</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21032</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>16</td>\n",
|
||
" <td>has_tv</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21033</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>16</td>\n",
|
||
" <td>id</td>\n",
|
||
" <td>neutral</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21034</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>17</td>\n",
|
||
" <td>has_tv</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21035</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>17</td>\n",
|
||
" <td>id</td>\n",
|
||
" <td>neutral</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21036</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>18</td>\n",
|
||
" <td>has_tv</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21037</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>18</td>\n",
|
||
" <td>id</td>\n",
|
||
" <td>neutral</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21038</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>19</td>\n",
|
||
" <td>has_tv</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21039</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>19</td>\n",
|
||
" <td>id</td>\n",
|
||
" <td>neutral</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21040</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>20</td>\n",
|
||
" <td>has_tv</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21041</th>\n",
|
||
" <td>499</td>\n",
|
||
" <td>20</td>\n",
|
||
" <td>id</td>\n",
|
||
" <td>infected</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>21042 rows × 4 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" agent_id tstep attribute value\n",
|
||
"0 env 0 prob_tv_spread 0.01\n",
|
||
"1 env 0 prob_neighbor_spread 0.1\n",
|
||
"2 env 1 prob_tv_spread 0.01\n",
|
||
"3 env 1 prob_neighbor_spread 0.1\n",
|
||
"4 env 2 prob_tv_spread 0.01\n",
|
||
"5 env 2 prob_neighbor_spread 0.1\n",
|
||
"6 env 3 prob_tv_spread 0.01\n",
|
||
"7 env 3 prob_neighbor_spread 0.1\n",
|
||
"8 env 4 prob_tv_spread 0.01\n",
|
||
"9 env 4 prob_neighbor_spread 0.1\n",
|
||
"10 env 5 prob_tv_spread 0.01\n",
|
||
"11 env 5 prob_neighbor_spread 0.1\n",
|
||
"12 env 6 prob_tv_spread 0.01\n",
|
||
"13 env 6 prob_neighbor_spread 0.1\n",
|
||
"14 env 7 prob_tv_spread 0.01\n",
|
||
"15 env 7 prob_neighbor_spread 0.1\n",
|
||
"16 env 8 prob_tv_spread 0.01\n",
|
||
"17 env 8 prob_neighbor_spread 0.1\n",
|
||
"18 env 9 prob_tv_spread 0.01\n",
|
||
"19 env 9 prob_neighbor_spread 0.1\n",
|
||
"20 env 10 prob_tv_spread 0.01\n",
|
||
"21 env 10 prob_neighbor_spread 0.1\n",
|
||
"22 env 11 prob_tv_spread 0.01\n",
|
||
"23 env 11 prob_neighbor_spread 0.1\n",
|
||
"24 env 12 prob_tv_spread 0.01\n",
|
||
"25 env 12 prob_neighbor_spread 0.1\n",
|
||
"26 env 13 prob_tv_spread 0.01\n",
|
||
"27 env 13 prob_neighbor_spread 0.1\n",
|
||
"28 env 14 prob_tv_spread 0.01\n",
|
||
"29 env 14 prob_neighbor_spread 0.1\n",
|
||
"... ... ... ... ...\n",
|
||
"21012 499 6 has_tv True\n",
|
||
"21013 499 6 id neutral\n",
|
||
"21014 499 7 has_tv True\n",
|
||
"21015 499 7 id neutral\n",
|
||
"21016 499 8 has_tv True\n",
|
||
"21017 499 8 id neutral\n",
|
||
"21018 499 9 has_tv True\n",
|
||
"21019 499 9 id neutral\n",
|
||
"21020 499 10 has_tv True\n",
|
||
"21021 499 10 id neutral\n",
|
||
"21022 499 11 has_tv True\n",
|
||
"21023 499 11 id neutral\n",
|
||
"21024 499 12 has_tv True\n",
|
||
"21025 499 12 id neutral\n",
|
||
"21026 499 13 has_tv True\n",
|
||
"21027 499 13 id neutral\n",
|
||
"21028 499 14 has_tv True\n",
|
||
"21029 499 14 id neutral\n",
|
||
"21030 499 15 has_tv True\n",
|
||
"21031 499 15 id neutral\n",
|
||
"21032 499 16 has_tv True\n",
|
||
"21033 499 16 id neutral\n",
|
||
"21034 499 17 has_tv True\n",
|
||
"21035 499 17 id neutral\n",
|
||
"21036 499 18 has_tv True\n",
|
||
"21037 499 18 id neutral\n",
|
||
"21038 499 19 has_tv True\n",
|
||
"21039 499 19 id neutral\n",
|
||
"21040 499 20 has_tv True\n",
|
||
"21041 499 20 id infected\n",
|
||
"\n",
|
||
"[21042 rows x 4 columns]"
|
||
]
|
||
},
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"config_file, df, config = list(analysis.get_data('soil_output/Spread_barabasi*prob_0.1*', process=False))[0]\n",
|
||
"df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T14:43:17.192030Z",
|
||
"start_time": "2017-07-03T16:43:16.601046+02:00"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style>\n",
|
||
" .dataframe thead tr:only-child th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: left;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th>value</th>\n",
|
||
" <th>0.01</th>\n",
|
||
" <th>0.1</th>\n",
|
||
" <th>False</th>\n",
|
||
" <th>True</th>\n",
|
||
" <th>infected</th>\n",
|
||
" <th>neutral</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>tstep</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>163.0</td>\n",
|
||
" <td>337.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>500.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>163.0</td>\n",
|
||
" <td>337.0</td>\n",
|
||
" <td>3.0</td>\n",
|
||
" <td>497.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>163.0</td>\n",
|
||
" <td>337.0</td>\n",
|
||
" <td>6.0</td>\n",
|
||
" <td>494.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>163.0</td>\n",
|
||
" <td>337.0</td>\n",
|
||
" <td>12.0</td>\n",
|
||
" <td>488.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>163.0</td>\n",
|
||
" <td>337.0</td>\n",
|
||
" <td>23.0</td>\n",
|
||
" <td>477.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>163.0</td>\n",
|
||
" <td>337.0</td>\n",
|
||
" <td>36.0</td>\n",
|
||
" <td>464.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>163.0</td>\n",
|
||
" <td>337.0</td>\n",
|
||
" <td>53.0</td>\n",
|
||
" <td>447.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>163.0</td>\n",
|
||
" <td>337.0</td>\n",
|
||
" <td>79.0</td>\n",
|
||
" <td>421.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>163.0</td>\n",
|
||
" <td>337.0</td>\n",
|
||
" <td>119.0</td>\n",
|
||
" <td>381.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>163.0</td>\n",
|
||
" <td>337.0</td>\n",
|
||
" <td>164.0</td>\n",
|
||
" <td>336.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>163.0</td>\n",
|
||
" <td>337.0</td>\n",
|
||
" <td>204.0</td>\n",
|
||
" <td>296.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>163.0</td>\n",
|
||
" <td>337.0</td>\n",
|
||
" <td>254.0</td>\n",
|
||
" <td>246.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>163.0</td>\n",
|
||
" <td>337.0</td>\n",
|
||
" <td>293.0</td>\n",
|
||
" <td>207.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>163.0</td>\n",
|
||
" <td>337.0</td>\n",
|
||
" <td>336.0</td>\n",
|
||
" <td>164.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>163.0</td>\n",
|
||
" <td>337.0</td>\n",
|
||
" <td>365.0</td>\n",
|
||
" <td>135.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>163.0</td>\n",
|
||
" <td>337.0</td>\n",
|
||
" <td>391.0</td>\n",
|
||
" <td>109.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>163.0</td>\n",
|
||
" <td>337.0</td>\n",
|
||
" <td>407.0</td>\n",
|
||
" <td>93.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>163.0</td>\n",
|
||
" <td>337.0</td>\n",
|
||
" <td>424.0</td>\n",
|
||
" <td>76.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>18</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>163.0</td>\n",
|
||
" <td>337.0</td>\n",
|
||
" <td>442.0</td>\n",
|
||
" <td>58.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>19</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>163.0</td>\n",
|
||
" <td>337.0</td>\n",
|
||
" <td>452.0</td>\n",
|
||
" <td>48.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>20</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>163.0</td>\n",
|
||
" <td>337.0</td>\n",
|
||
" <td>464.0</td>\n",
|
||
" <td>36.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
"value 0.01 0.1 False True infected neutral\n",
|
||
"tstep \n",
|
||
"0 1.0 1.0 163.0 337.0 0.0 500.0\n",
|
||
"1 1.0 1.0 163.0 337.0 3.0 497.0\n",
|
||
"2 1.0 1.0 163.0 337.0 6.0 494.0\n",
|
||
"3 1.0 1.0 163.0 337.0 12.0 488.0\n",
|
||
"4 1.0 1.0 163.0 337.0 23.0 477.0\n",
|
||
"5 1.0 1.0 163.0 337.0 36.0 464.0\n",
|
||
"6 1.0 1.0 163.0 337.0 53.0 447.0\n",
|
||
"7 1.0 1.0 163.0 337.0 79.0 421.0\n",
|
||
"8 1.0 1.0 163.0 337.0 119.0 381.0\n",
|
||
"9 1.0 1.0 163.0 337.0 164.0 336.0\n",
|
||
"10 1.0 1.0 163.0 337.0 204.0 296.0\n",
|
||
"11 1.0 1.0 163.0 337.0 254.0 246.0\n",
|
||
"12 1.0 1.0 163.0 337.0 293.0 207.0\n",
|
||
"13 1.0 1.0 163.0 337.0 336.0 164.0\n",
|
||
"14 1.0 1.0 163.0 337.0 365.0 135.0\n",
|
||
"15 1.0 1.0 163.0 337.0 391.0 109.0\n",
|
||
"16 1.0 1.0 163.0 337.0 407.0 93.0\n",
|
||
"17 1.0 1.0 163.0 337.0 424.0 76.0\n",
|
||
"18 1.0 1.0 163.0 337.0 442.0 58.0\n",
|
||
"19 1.0 1.0 163.0 337.0 452.0 48.0\n",
|
||
"20 1.0 1.0 163.0 337.0 464.0 36.0"
|
||
]
|
||
},
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"list(analysis.get_data('soil_output/Spread_barabasi*prob_0.1*', process=True))[0][1]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"If you don't want to work with pandas, you can also use some pre-defined functions from soil to conveniently plot the results:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T14:43:20.937324Z",
|
||
"start_time": "2017-07-03T16:43:17.193845+02:00"
|
||
},
|
||
"scrolled": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xt8XVWd9/HPL/d70qRJ79KClWKlLaUgCgpjBRG5ODMF\nO3a4KIrXQcfLwMgo4IzP4KMjo46jg+IAM5WLZRCGwWfgBa0oN20RkFKwrbaQ3hLSNs39up4/1jrJ\nSXJOctqcc5J0f9+v136dfVl7n7V3TtZv77XXXtucc4iISHTlTHQGRERkYikQiIhEnAKBiEjEKRCI\niEScAoGISMQpEIiIRJwCwVHMzG4zs38YI81ZZlY/mfJ0BNt8g5m1mlnuOLYx5DiY2Q4ze3d6cjh5\nmdkVZvarSZCPSBzvyUqBIA3M7Awze9LMms1sv5k9YWanTHS+osI596pzrsw51zfReUlGBV1mmNlK\nM3vZzNrNbL2ZHTNK2vkhTXtYR3+PQIFgnMysAngQ+C5QDcwBbgS6DnM7ZmZT+u9hZnkTnYfJJtPH\nZCoc80zl0cymA/8FfBn/v7cRuHuUVe4EfgvUANcB68ysNhN5m2qmdMEzSbwJwDl3p3OuzznX4Zx7\n2Dn3QrjsfsLMvhuuFl42s5WxFc1sg5l9zcyeANqBY82s0sxuNbM9ZrbLzP4hVuVhZseZ2WNm1mRm\nr5vZWjOritveSWb2rJm1mNndQFGqO2FmXwrb3GFma+Lmv8/Mfmtmh8zsNTO7IW7ZfDNzZnalmb0K\nPBbm/9TM9oZ9ftzMFg/7uulm9kjI5y/iz+LM7Nvhew6Z2SYze0fcslPNbGNYts/MvjUsH6MWOGb2\nITPbEr73D2b2sTEOyylm9pKZHTCzfzezgeNpZueb2XNmdjBcDS6JW7bDzK4xsxeANjO7E3gD8N+h\nCutvxsjnZWa2M/ydvxx/NWFmN5jZOjP7TzM7BFwRjstTIS97zOxfzKwgbnvOzK4O+/y6mX1j+EmH\nmX0z7Ocfzey9YxyX2G/3H83s1+HvfL+ZVYdlyX4XF5rZ5pDPDWZ2QqrHO4k/AzY7537qnOsEbgCW\nmtmiBPl9E7AcuD78j94L/A7487H2NRKccxrGMQAVQBNwO/BeYFrcsiuAXuCvgXzgA0AzUB2WbwBe\nBRYDeSHNz4B/A0qBOuDXwMdC+jcCZwOFQC3wOPDPYVkBsDPuu1YBPcA/jJH/s0IevxW2eybQBhwf\nt/xE/EnDEmAf8P6wbD7ggDtCfovD/A8D5WF7/ww8F/d9twEtwDvD8m8Dv4pb/pf4M7Y84PPAXqAo\nLHsKuDSMlwGnDctH3hj7+j7gOMDCfrYDy+P2sz4u7Q7gRWAe/mzzidixxBcoDcBbgVzg8pC+MG7d\n58K6xXHz3p3C7+nNQCtwRvibfjP8Hd8dlt8Qpt8f/ibFwMnAaeGYzQe2AJ+N26YD1of9eAPwe+Aj\ncb/RHuCjYV8+AewGbIx8bgB2AW8Jf/t7gf9M9rvAnzC14X+/+cDfANuAgrGO9yh5+Dbw/WHzXgT+\nPEHaPwW2DJv3L8B3J7oMmQzDhGfgaBiAE/AFXD2+UH0AmBH+yYb8U+EL9lhhtgH4atyyGfgqpeK4\neX8BrE/yve8HfhvG35ngu55M4Z/prJDn0rh59wBfTpL+n4Gbw3jsH/7YUbZfFdJUhunbgLvilpcB\nfcC8JOsfAJaG8cfx1W7Th6WJ5WPUQJBg2z8DPhN3HIYHgo/HTZ8HbA/j3wf+fti2XgHOjFv3w8OW\n7yC1QPAV4M646RKgm6GB4PExtvFZ4L64aQecGzf9SeDRMH4FsG3Y9zlg5hjfsQG4KW76zSGfuYl+\nF/jqm3vipnPwgeSssY73KHm4NT4PYd4TwBUJ0l4KPD1s3teA2w7nN3O0DqoaSgPn3Bbn3BXOubn4\nM6TZ+AITYJcLv7pgZ1ge81rc+DH4s6U94fL5IP7qoA7AzOrM7K5QZXQI+E9gelh3dpLvSsUB51xb\nojya2VvN32BrNLNm4ONx3zliH8ws18xuMrPtIY87wqLpidI751qB/XHf9/lQfdMc9r8ybt0r8WeW\nL5vZb8zs/BT3L5a395rZ0+Zv6B/EFzbD9yXhfjH073YM8PnY3yhsax7J/66HYzZDj087/oozWb4w\nszeZ2YOhOu4Q8H8Y5W/EyN/g3mHfBz5Aj2X4NvNJ8ncO3zfwe3TO9Yflc1LMYyKt+CvyeBX4K87x\npI0cBYI0c869jD/rfUuYNcfMLC7JG/Bn7gOrxI2/hr8imO6cqwpDhXMuVsf+jyH9EudcBb4aJbbt\nPUm+KxXTzKw0SR5/gr/CmeecqwR+EPedifbhg8BFwLvxhfj8MD9+nXmxETMrw1cF7A73A64BLsFX\nsVXhq9IMwDm31Tn3F/jA+HX8zb74fCdlZoX46otvAjPCth9KsC/x5sWNxx+T14Cvxf2NqpxzJc65\nO+PSD+/WN9VufvcAc+PyXYyvKhttW98HXgYWht/Flxi5X8n2ZTyGb7MHeD1JPnfjAyjgG0eE9XeN\nI4+bgaVx2yzFV/1tTpL2WDMrj5u3NEnayFEgGCczWxTOYueG6Xn46pynQ5I64Gozyzezi/HVSA8l\n2pZzbg/wMPBPZlZhZjnmbxCfGZKU489sDprZHOCLcas/ha/iudrM8szsz4BTD2NXbjSzglAYnw/8\nNO479zvnOs3sVHxBP5pyfDBrwlcz/J8Eac4z3+S2APh74Bnn3Gth3V6gEcgzs68QdxZnZn9pZrXh\nbPJgmJ1qk9EC/D2JRqA33BA9Z4x1PmVmc8NN0C8x2CLlh8DHw9WSmVmp+Zvq5ck3xT7g2BTyuQ64\nwMzeHo7PjYwerMAft0NAa7hR+okEab5oZtPC7/MzjN66JlV/aWZvNrMS4KvAOpe8Ce89wPvMN/fM\nx9//6cJXX8YkO97J3Ae8xcz+PNxY/grwQjgZG8I593v8fZvrzazIzP4Uf8/r3tR39+ilQDB+Lfib\nhs+YWRs+ALyI/6EDPAMsxJ8pfQ1Y5Zwbfqkf7zJ8ofUSvn58HTArLLsRf6OyGfgffNM5AJxz3fhW\nFFeE9T4Qv3wMe8M6u4G1+Lra2D/TJ4GvmlkL/h/tnjG2dQf+sn5X2IenE6T5CXA9vkroZCDWSul/\ngZ/jb2buBDoZWl1wLrDZzFrxNwpXO99aZEzOuRbg6pD/A/iA9sAYq/0EH5j/EIZ/CNvaiL+5+i9h\nW9vwx300/wj8XahK+sIo+dwM/BVwF/7qoAV/Y3q05shfCPvTgg9SiQrQ+4FN+MLwf/D16+P1H/ir\n3734FmpXJ0vonHsFfwX7Xfz/wgXABeF3G5PweI+yzUZ8q5+v4f8ObwVWx5ab2Q/M7Adxq6wGVoS0\nN+H/FxtT2M+jng2tUpZ0MrMr8K0zzpjovMjUFKrODuKrff54hNtwYf1taczXBnwroR+la5sycXRF\nIDLJmNkFZlYS6ry/iW/vvmNicyVHMwWCCDD/sFhrguHnE523dEuyn60W92DaRDOzNUnyGLtxeRG+\nmm43vlpxtZuAS/fJcCyj9NudSKoaEhGJOF0RiIhE3KTosGr69Olu/vz5E50NEZEpZdOmTa8758bd\ncd6kCATz589n48aNE50NEZEpxcxS7T1gVKoaEhGJOAUCEZGIUyAQEYk4BQIRkYhTIBARibiUAoH5\nV+X9zvyr+TaGedXmXze4NXxOC/PNzL5jZtvM7AUzW57JHRARkfE5nCuCP3HOLXPOrQjT1+LfcrQQ\neDRMg39d48IwXIXvK11ERCap8TxHcBH+9X7g39e7Af9SkYuAO0LfKE+bWZWZzQp97SfWshd++U+Q\nWxCG/MMfLyyHwkrIUW2XiMjhSDUQOODh0J3tvznnbsG/5WkP+BeqmFldSDuHoX3I14d5QwKBmV2F\nv2Lg5Fk58OhXj3wvBjaaA0WVUFwNxdNGH0ri0hRVQk7u+L9fRGQKSjUQnO6c2x0K+0fMbMQbgOIk\nepvSiJ7tQjC5BWDFihWOv3sC+rqhryd8HsZ4bxd0tUDHgaFDexM0bfXjnc2j72FRFUw7BqqPDcNx\ng+NldWBjvSRKRGRqSikQOOd2h88GM7sP/wrEfbEqHzObhX+LEvgrgPh3j84llfej5hX6IVP6+3ww\nGBIo9seNvw4HdsCe5+GlByD+jXsFZVC9YGSAqDkOymYoSIjIlDZmIAgvx8hxzrWE8XPw7yd9ALgc\n/8q3y/GvwiPM/7SZ3YV/dVzzqPcHsiUn11cHlVSPnbavBw6+Cvv/CPv/APu3+899m+Hl/4H+3sG0\n+SUhMCyAafOhbCaUz/RXEWUz/GdRlYKFiExaqVwRzADuM1+Q5QE/cc79PzP7DXCPmV0JvApcHNI/\nBJyHf49rO/ChtOc603Lz/dl+zXEjl/X1QvNrIUD8IQSL7dD4Cvz+YehL8GrZ3MLBoDD8s3zm4Hhp\nHeQXZX7/RETiTIoX06xYscIdFb2POuern1oboHXfsCHMawnT7U0kuHUC+aVQUAqFZf6zIPY5fDzR\nsjBdVOlvghdWqBWVyFHMzDbFNek/YpOiG+qjhhkUV/mh9k2jp+3rgbbXhwaLln3QeRC6W6G7LQyt\n/h5Gc/3gdHebv1E+Zn5yfLVUopZSI4bqkHe1ohKJGgWCiZKbDxWz/HAkeruhpw26hgWN7taRN8Vj\nN8ZbG3wVVsdB6BqlFZXlQPksqJwLlfPCZxivCtNFlUeWbxGZdBQIpqq8Aj8UTzuy9ft6Q8DYPzJo\ntL0Oh3b7eyG7NsJL90N/z9D1CytGDxRlM3ywE5FJT4EgqnLzoLTGD2Pp74e2Bjj4mg8OzfVxw6tQ\n/2sfQIaznARPgh/GU+OFFclvsivIiKSNAoGMLSfHt24qnwnzTkmcpqsVDu0aDBStjSk+GNjlP3s6\n/BVKbHlvF3QdShxgAEpqkgSJ+KEuPc+m5BZATp6aAMtRS4FA0qOwDGqP90M69XZBW+Nga6uBFlh7\nB1tivfqUX56o6W46jXolE/ssHLm8oBRKa4c9XxICVWGFAoxMOAUCmdzyCgfvQYwmYdPdhpH3Ng6X\nc34bA1cyqXR70u1v2sfmd7Ukz0teceKrmvIZQ+cVT/MPLypoSAYoEMjR4XCa7k4E53w112jPmDRt\nh51P+hv4CVkKz5IkWVZY7gNK5VwfVBRQJI4CgUg2mA12cVK3aPS0vd2+Omzg+ZK9/mpnSDPhuPH2\nJt8lSncbdLf4z/huUIYrKItr6RVr+RXX+qtitm7GR4wCgchkk1cAlXP8cCScC9VTcUGjqwVa9gy2\n9jr4qv/c/ZzvcDHekOdIhjURLqmJ6769yrc+kylPf0WRo43ZYG++qXSy2N0+tMVXc/1gU+Fdz8KW\n/07+JHthxeAT6aO9ByT2VHtBKYl7qj8MRRW+qkvSRoFAJOoKSmD6Qj8kEnuO5NCu8NDhwaFduMcP\nza8Njrv+zOW5qGrolUrsQcbYvLKZ6mfrMCgQiMjo4p8jSVV/v79fMfzdH91t48yMG+x7q7neB56d\nT47sMiUn39/riAWGqmFPwJfPVNPdOAoEIpJ+OeG1sUWV/j0dmdbZDM27Bp92j3/6fcevoGX3yCuU\nvKLkDyIOH88ryPw+TCAFAhGZ+mJBZ8abEy/v6x16s7x172DT3Za9YzfdLZ6WJEgMe0iwpHpKXmUo\nEIjI0S83z1cPVc0bPd3wprvxz3nExl/7tR/v7Ry5fk5+CAx1yYNF7GHB/OLM7OsRUCAQEYlJtemu\nc4NPjLfuG9rlSezz0C7f6qqtkYQvoSquhtpFvluWuhNCFy2LJuQ96AoEIiKHy8w3Yy2qgOlvHD1t\nX69/6G/I1cVeOLDTvx9k83/Bprib3UVVgwGidpF/ALF2kX+2I0MBQoFARCSTcvN8dVD5jMTLnQsv\njdriA0Pjy9DwMmx5AJ69fTBdYcVgcIgNaaJAICIykcwGA8WxZw3Od86/JGp4gHjl5/Db/0hrFhQI\nREQmIzMoq/XDgncOXdb2ug8MN74jLV+lQCAiMtWUTofSM9K2OT2DLSIScQoEIiIRp0AgIhJxCgQi\nIhGnQCAiEnEKBCIiEadAICIScQoEIiIRp0AgIhJxKQcCM8s1s9+a2YNheoGZPWNmW83sbjMrCPML\nw/S2sHx+ZrIuIiLpcDhXBJ8BtsRNfx242Tm3EDgAXBnmXwkccM69Ebg5pBMRkUkqpUBgZnOB9wE/\nCtMGvAtYF5LcDrw/jF8UpgnLV4b0IiIyCaV6RfDPwN8Asbc/1wAHnXO9YboeiL3SZw7wGkBY3hzS\nD2FmV5nZRjPb2NjYeITZFxGR8RozEJjZ+UCDc25T/OwESV0KywZnOHeLc26Fc25FbW1tSpkVEZH0\nS6Ub6tOBC83sPKAIqMBfIVSZWV44658L7A7p64F5QL2Z5QGVwP6051xERNJizCsC59zfOufmOufm\nA6uBx5xza4D1wKqQ7HLg/jD+QJgmLH/MOZfgzc0iIjIZjOc5gmuAz5nZNvw9gFvD/FuBmjD/c8C1\n48uiiIhk0mG9ocw5twHYEMb/AJyaIE0ncHEa8iYiIlmgJ4tFRCJOgUBEJOIUCEREIk6BQEQk4hQI\nREQiToFARCTiFAhERCJOgUBEJOIUCEREIk6BQEQk4hQIREQiToFARCTiFAhERCJOgUBEJOIUCERE\nIk6BQEQk4hQIREQiToFARCTiFAhERCJOgUBEJOIUCEREIk6BQEQk4hQIREQiToFARCTiFAhERCJO\ngUBEJOIUCEREIk6BQEQk4hQIREQiLm+iMyAi0dPT00N9fT2dnZ0TnZUpoaioiLlz55Kfn5+R7Y8Z\nCMysCHgcKAzp1znnrjezBcBdQDXwLHCpc67bzAqBO4CTgSbgA865HRnJvYhMSfX19ZSXlzN//nzM\nbKKzM6k552hqaqK+vp4FCxZk5DtSqRrqAt7lnFsKLAPONbPTgK8DNzvnFgIHgCtD+iuBA865NwI3\nh3QiIgM6OzupqalREEiBmVFTU5PRq6cxA4HzWsNkfhgc8C5gXZh/O/D+MH5RmCYsX2n6a4vIMCoW\nUpfpY5XSzWIzyzWz54AG4BFgO3DQOdcbktQDc8L4HOA1gLC8GahJsM2rzGyjmW1sbGwc316IiMgR\nSykQOOf6nHPLgLnAqcAJiZKFz0Shy42Y4dwtzrkVzrkVtbW1qeZXRCQjysrKJjoLE+awmo865w4C\nG4DTgCozi91sngvsDuP1wDyAsLwS2J+OzIqISPqNGQjMrNbMqsJ4MfBuYAuwHlgVkl0O3B/GHwjT\nhOWPOedGXBGIiGTSNddcw7/+678OTN9www3ceOONrFy5kuXLl3PiiSdy//33j1hvw4YNnH/++QPT\nn/70p7ntttsA2LRpE2eeeSYnn3wy73nPe9izZ0/G9yMbUrkimAWsN7MXgN8AjzjnHgSuAT5nZtvw\n9wBuDelvBWrC/M8B16Y/2yIio1u9ejV33333wPQ999zDhz70Ie677z6effZZ1q9fz+c//3lSPU/t\n6enhr/7qr1i3bh2bNm3iwx/+MNddd12msp9VYz5H4Jx7ATgpwfw/4O8XDJ/fCVycltyJiByhk046\niYaGBnbv3k1jYyPTpk1j1qxZ/PVf/zWPP/44OTk57Nq1i3379jFz5swxt/fKK6/w4osvcvbZZwPQ\n19fHrFmzMr0bWaEni0XkqLVq1SrWrVvH3r17Wb16NWvXrqWxsZFNmzaRn5/P/PnzR7TPz8vLo7+/\nf2A6ttw5x+LFi3nqqaeyug/ZoL6GROSotXr1au666y7WrVvHqlWraG5upq6ujvz8fNavX8/OnTtH\nrHPMMcfw0ksv0dXVRXNzM48++igAxx9/PI2NjQOBoKenh82bN2d1fzJFVwQictRavHgxLS0tzJkz\nh1mzZrFmzRouuOACVqxYwbJly1i0aNGIdebNm8cll1zCkiVLWLhwISed5GvGCwoKWLduHVdffTXN\nzc309vby2c9+lsWLF2d7t9LOJkODnhUrVriNGzdOdDZEJEu2bNnCCSckehxJkkl0zMxsk3NuxXi3\nraohEZGIUyAQEYk4BQIRkYhTIBARiTgFAhGRiFMgEBGJOAUCEYmkt7/97WOm+eUvf8nixYtZtmwZ\nHR0dh7X9n/3sZ7z00kuHna+J6A5bgUBEIunJJ58cM83atWv5whe+wHPPPUdxcfFhbf9IA8FEUCAQ\nkUiKnXlv2LCBs846i1WrVrFo0SLWrFmDc44f/ehH3HPPPXz1q19lzZo1AHzjG9/glFNOYcmSJVx/\n/fUD27rjjjtYsmQJS5cu5dJLL+XJJ5/kgQce4Itf/CLLli1j+/btbN++nXPPPZeTTz6Zd7zjHbz8\n8ssA/PGPf+Rtb3sbp5xyCl/+8pezfyBQFxMiMsFu/O/NvLT7UFq3+ebZFVx/QepdP/z2t79l8+bN\nzJ49m9NPP50nnniCj3zkI/zqV7/i/PPPZ9WqVTz88MNs3bqVX//61zjnuPDCC3n88cepqanha1/7\nGk888QTTp09n//79VFdXc+GFFw6sC7By5Up+8IMfsHDhQp555hk++clP8thjj/GZz3yGT3ziE1x2\n2WV873vfS+txSJUCgYhE3qmnnsrcuXMBWLZsGTt27OCMM84Ykubhhx/m4YcfHuh7qLW1la1bt/L8\n88+zatUqpk+fDkB1dfWI7be2tvLkk09y8cWDPfR3dXUB8MQTT3DvvfcCcOmll3LNNdekfwfHoEAg\nIhPqcM7cM6WwsHBgPDc3l97e3hFpnHP87d/+LR/72MeGzP/Od76DWaJXtQ/q7++nqqqK5557LuHy\nsdbPNN0jEBFJwXve8x5+/OMf09raCsCuXbtoaGhg5cqV3HPPPTQ1NQGwf79/RXt5eTktLS0AVFRU\nsGDBAn76058CPqg8//zzAJx++uncddddgL85PREUCEREUnDOOefwwQ9+kLe97W2ceOKJrFq1ipaW\nFhYvXsx1113HmWeeydKlS/nc5z4H+HchfOMb3+Ckk05i+/btrF27lltvvZWlS5eyePHigfclf/vb\n3+Z73/sep5xyCs3NzROyb+qGWkSyTt1QHz51Qy0iIhmjQCAiEnEKBCIiEadAICIScQoEIiIRp0Ag\nIhJxCgQiIuOwY8cOfvKTnxzRuhPR5XQiCgQiIuMwWiBI1FXFZKRAICKRtGPHDk444QQ++tGPsnjx\nYs455xw6OjqSdhd9xRVXsG7duoH1Y2fz1157Lb/85S9ZtmwZN998M7fddhsXX3wxF1xwAeeccw6t\nra2sXLmS5cuXc+KJJw48UTyZqNM5EZlYP78W9v4uvduceSK896Yxk23dupU777yTH/7wh1xyySXc\ne++9/Pu//3vC7qKTuemmm/jmN7/Jgw8+CMBtt93GU089xQsvvEB1dTW9vb3cd999VFRU8Prrr3Pa\naadx4YUXTnhHc/EUCEQkshYsWMCyZcsAOPnkk9mxY0fS7qIPx9lnnz3QHbVzji996Us8/vjj5OTk\nsGvXLvbt28fMmTPTsxNpoEAgIhMrhTP3TBne/fS+ffuSdhedl5dHf38/4Av37u7upNstLS0dGF+7\ndi2NjY1s2rSJ/Px85s+fT2dnZxr3YvzGvEdgZvPMbL2ZbTGzzWb2mTC/2sweMbOt4XNamG9m9h0z\n22ZmL5jZ8kzvhIhIOozWXfT8+fPZtGkTAPfffz89PT3A0O6mE2lubqauro78/HzWr1/Pzp07M7wX\nhy+Vm8W9wOedcycApwGfMrM3A9cCjzrnFgKPhmmA9wILw3AV8P2051pEJEOSdRf90Y9+lF/84hec\neuqpPPPMMwNn/UuWLCEvL4+lS5dy8803j9jemjVr2LhxIytWrGDt2rUsWrQoq/uTisPuhtrM7gf+\nJQxnOef2mNksYINz7ngz+7cwfmdI/0osXbJtqhtqkWhRN9SHb9J0Q21m84GTgGeAGbHCPXzWhWRz\ngNfiVqsP84Zv6yoz22hmGxsbGw8/5yIikhYpBwIzKwPuBT7rnDs0WtIE80ZcdjjnbnHOrXDOrait\nrU01GyIikmYpBQIzy8cHgbXOuf8Ks/eFKiHCZ0OYXw/Mi1t9LrA7PdkVkaPFZHg74lSR6WOVSqsh\nA24FtjjnvhW36AHg8jB+OXB/3PzLQuuh04Dm0e4PiEj0FBUV0dTUpGCQAuccTU1NFBUVZew7UnmO\n4HTgUuB3ZhZrXPsl4CbgHjO7EngViD2B8RBwHrANaAc+lNYci8iUN3fuXOrr69H9wdQUFRUxd+7c\njG1/zEDgnPsViev9AVYmSO+AT40zXyJyFMvPz2fBggUTnQ0J1OmciEjEKRCIiEScAoGISMQpEIiI\nRJwCgYhIxCkQiIhEnAKBiEjEKRCIiEScAoGISMQpEIiIRJwCgYhIxCkQiIhEnAKBiEjEKRCIiESc\nAoGISMQpEIiIRJwCgYhIxCkQiIhEnAKBiEjEKRCIiEScAoGISMQpEIiIRJwCgYhIxCkQiIhEnAKB\niEjEKRCIiEScAoGISMQpEIiIRJwCgYhIxCkQiIhE3JiBwMx+bGYNZvZi3LxqM3vEzLaGz2lhvpnZ\nd8xsm5m9YGbLM5l5EREZv1SuCG4Dzh0271rgUefcQuDRMA3wXmBhGK4Cvp+ebIqISKaMGQicc48D\n+4fNvgi4PYzfDrw/bv4dznsaqDKzWenKrIiIpN+R3iOY4ZzbAxA+68L8OcBrcenqw7wRzOwqM9to\nZhsbGxuPMBsiIjJe6b5ZbAnmuUQJnXO3OOdWOOdW1NbWpjkbIiKSqiMNBPtiVT7hsyHMrwfmxaWb\nC+w+8uyJiEimHWkgeAC4PIxfDtwfN/+y0HroNKA5VoUkIiKTU95YCczsTuAsYLqZ1QPXAzcB95jZ\nlcCrwMUh+UPAecA2oB34UAbyLCIiaTRmIHDO/UWSRSsTpHXAp8abKRERyR49WSwiEnEKBCIiEadA\nICIScQoEIiIRp0AgIhJxCgQiIhGnQCAiEnFjPkcgIiKTi3OOprbutG1PgUBEZJJyztHY0sXWhla2\n7mvh9w2tbNvXytaGFg6096TtexQIREQmmHOOfYe62NrQwtZQ0PvPVpo7Bgv8iqI83jSjnHPfMpM3\n1pXzka97xd4IAAAOQ0lEQVSn5/sVCEREsqCzp4/Gli4aW7toONRF/YH2wUK/oZWWzt6BtFUl+byp\nrpz3LZnFm+rKWDijnIV1ZdSWF2I22Nv/R9KUNwUCEZEj1NfvONDeTcMhX8A3tsQNrV00HOocmB9f\n0MfUlBbwxroy3r9sDgtnlPHGujLeNKOcmtKCIQV+pikQiIgM09bVS0N8od4yWKDHz29q66avf+S7\nt0oLcqktL6S2vJATZlbwzoV+vLascGD+rMoiasoKJ2DvRlIgEJFIcM5xoL2H3Qc7aGjpHCjMG4ad\nxTe2dNHe3Tdi/dwcY3pZwUBBvnh2BXXlRQPTteWF1JUXMr2skNLCqVW0Tq3ciogk0d3bz97mTuoP\ntrP7YCe7D3aw+2AHu8Kw+2AHnT39I9arKMqjrqKI2rJCls6tGizYw9l7XYUfn1ZSQE5O9qprskmB\nQEQyxjlHR08fB9p7ONjeTUeCM+3D2h5woK3bF/LNnb6QP+AL+cbWLtywWprpZYXMqSpi0cxy3nV8\nHbOripldVcSMCn8mP72skKL83HHl6WigQCAiKWnr6uVAezcH23v80OHHmzt8IX+wvYcD7T00h/kH\nO3pobu+hu2/kWXg6FOTlMCcU7GcdXxsK+WLmhGFmZZEK+RQpEIgIvX39NLR0jahKiVWx7DrQQUvX\nyFYvMcX5uUwryaeypICq4nzeWFdGVUk+lcUFVJXk+2XFBZQU5DLexjAVRfnMmVac9ZY1RzMFApGj\nXG9fP23dfew71DlQwMeqU3Yf9PP2Huoc0fqlqiSf2ZXFzKsu4bRja5hZWUR1qS/oq0p8AV9VnE9F\ncb7OvKc4BQKRSSq+CWNzRw/t3b20dfUN/ezupb2rz39299HaNXS6rauXrt6RVTN5OcbMyiJmVxXz\n1gXVA9Uqs6uKmDutmFmVxVOu5YscOf2lRbKot6+fprbYA0idQx5AakihCWO8ovwcSgvyKCnM9Z8F\nuZQV5lFXXjhsfh6lhbnUVRQxp8oX/nXlReQepS1g5PApEMhRpa/f0dTmC9LXW7vpHeeNyn7nC+/u\nvn56+hw9ff309PXT3Ttsuq+fnt5h032Ont5+2rp7Bwr7/e3dI1q2AFQW5w80WVw6t4q6uLbpteWF\nVBbnU1qYR1mhL/BLCvJUkEvaKBDIpOeco7Wrd9ij+yMf6W9o6WJ/WxcJHvTMKDMoyM3xQ14O+bk5\n5OcZ+WFeUX4u86pLOPmYaQnaqBcxvayAwjzVscvEUSCQtOnp62ffoU52H+zk9daulM6cu3r7B8Z7\n+lxY7qcPdQ4W/h09I6tJ8nJsyOP6S+ZWDjzdGWsjXpA3vncvGTakUM/PzSE/18jPG5zWmblMdQoE\nkrJDnT1xrU062BVrWhim9x3qTPlsPC/HBgrVgbPoWCEbzqzLCvNY/oaqIVUktWVFA4V9ZXH+Ufuk\np0g2KRBEXH+/o6Wzd+DhoIMdPf7JzeahTQx3HxzZjrwgN4dZVUXMrizm7cdNZ05VEXOm+dYnteWF\nFObl+oI+VsjnhYI+J0cFuMgkokBwFOjrd7THNRds7hh8qvNgezcHO8KToHHjsadBmzt6kp7FV5Xk\nM6eqmDfUlPC242qYXVXEnKqS8FnM9LJCFegiRwEFggnW0d03pCfEgx09tHWFQj2+jfiwtuHt3b4d\neWtXb8KOtIYrL8pjWngIqLI4n3nVJeHBID9dVVLAtJL8gadBZ1UWqR25SEToPz0DYk0YE76sYlhb\n8dZRHtsvzMuhNDQXjG8XXlNaMDg/bnlsvLI4n8rw1GdVSQEVRXnk5Y7vpqmIHL0UCI5Af79jz6FO\ndr7exo6mdnY0tbHj9TZeO9Dh24onacJYXpQ30HRw8eyKcNOzaEiTwqoS3168tCBXhbeIZIUCQRJ9\n/Y7dBzvY2dTOH5vaBgr9nU1t7NzfTnfcY/sFeTkcU13CG6pLWDavktpY4V5WOKQ5o/pjEZHJKCOB\nwMzOBb4N5AI/cs7dlInvORLOOVq6emlu7xnsUrejh/2tXby6v4OdTW3saGrjtf0dQ7rPLczLYX5N\nKcfWlvKuRXUcU1PK/JoS5k8vZWZFkW6aisiUlfZAYGa5wPeAs4F64Ddm9oBz7qUj2V5fv4t7EGnw\n4aTu2ENIvf4hpJbOWEuYwb7SBwr7WAuaDp8m0TtGwXele0xNCQvryjn7zTOZX1PCMTWlLJheSl25\nWsiIyNEpE1cEpwLbnHN/ADCzu4CLgKSB4Pf7Wjjj648NPF3a0ztY0B9pdwHlRXmhm9yCgWaQ8dNV\nJQUDrWZi0+rfXESiKBOBYA7wWtx0PfDW4YnM7CrgKoCK2cdy6oLquEf4fV8tQ6YTPIEa/6BSWWHe\nQP/olcX5utEqIpKiTASCRKfUI87rnXO3ALcArFixwn3rkmUZyIqIiIwlE6fN9cC8uOm5wO4MfI+I\niKRBJgLBb4CFZrbAzAqA1cADGfgeERFJg7RXDTnnes3s08D/4puP/tg5tznd3yMiIumRkecInHMP\nAQ9lYtsiIpJealojIhJxCgQiIhGnQCAiEnEKBCIiEWfOHWEfDunMhFkL8MpE5yMF04HXJzoTKVA+\n02cq5BGUz3SbKvk83jlXPt6NTJZuqF9xzq2Y6EyMxcw2Kp/pMxXyORXyCMpnuk2lfKZjO6oaEhGJ\nOAUCEZGImyyB4JaJzkCKlM/0mgr5nAp5BOUz3SKVz0lxs1hERCbOZLkiEBGRCaJAICIScVkNBGZ2\nrpm9YmbbzOzaBMsLzezusPwZM5ufzfyFPMwzs/VmtsXMNpvZZxKkOcvMms3suTB8Jdv5DPnYYWa/\nC3kY0YzMvO+E4/mCmS3Pcv6OjztGz5nZITP77LA0E3YszezHZtZgZi/Gzas2s0fMbGv4nJZk3ctD\nmq1mdnmW8/gNM3s5/E3vM7OqJOuO+vvIQj5vMLNdcX/b85KsO2q5kIV83h2Xxx1m9lySdbN5PBOW\nQxn7fTrnsjLgu6TeDhwLFADPA28eluaTwA/C+Grg7mzlLy4Ps4DlYbwc+H2CfJ4FPJjtvCXI6w5g\n+ijLzwN+jn9r3GnAMxOY11xgL3DMZDmWwDuB5cCLcfP+L3BtGL8W+HqC9aqBP4TPaWF8WhbzeA6Q\nF8a/niiPqfw+spDPG4AvpPC7GLVcyHQ+hy3/J+Ark+B4JiyHMvX7zOYVwcBL7Z1z3UDspfbxLgJu\nD+PrgJWW5bfJO+f2OOeeDeMtwBb8e5inoouAO5z3NFBlZrMmKC8rge3OuZ0T9P0jOOceB/YPmx3/\nG7wdeH+CVd8DPOKc2++cOwA8ApybrTw65x52zvWGyafxbwGcUEmOZSpSKRfSZrR8hrLmEuDOTH1/\nqkYphzLy+8xmIEj0UvvhBexAmvBDbwZqspK7BELV1EnAMwkWv83Mnjezn5vZ4qxmbJADHjazTWZ2\nVYLlqRzzbFlN8n+wyXAsY2Y45/aA/2cE6hKkmUzH9cP4q75Exvp9ZMOnQxXWj5NUY0ymY/kOYJ9z\nbmuS5RNyPIeVQxn5fWYzEKTyUvuUXnyfDWZWBtwLfNY5d2jY4mfxVRxLge8CP8t2/oLTnXPLgfcC\nnzKzdw5bPimOp/lXll4I/DTB4slyLA/HZDmu1wG9wNokScb6fWTa94HjgGXAHny1y3CT4lgGf8Ho\nVwNZP55jlENJV0swb9Rjms1AkMpL7QfSmFkeUMmRXW6Oi5nl4w/+Wufcfw1f7pw75JxrDeMPAflm\nNj3L2cQ5tzt8NgD34S+z46VyzLPhvcCzzrl9wxdMlmMZZ1+s+ix8NiRIM+HHNdwAPB9Y40LF8HAp\n/D4yyjm3zznX55zrB36Y5Psn/FjCQHnzZ8DdydJk+3gmKYcy8vvMZiBI5aX2DwCxO9yrgMeS/cgz\nJdQT3gpscc59K0mambF7F2Z2Kv44NmUvl2BmpWZWHhvH30B8cViyB4DLzDsNaI5dVmZZ0jOtyXAs\nh4n/DV4O3J8gzf8C55jZtFDdcU6YlxVmdi5wDXChc649SZpUfh8ZNex+1J8m+f5UyoVseDfwsnOu\nPtHCbB/PUcqhzPw+s3EHPO5u9nn4u9/bgevCvK/if9AARfjqg23Ar4Fjs5m/kIcz8JdRLwDPheE8\n4OPAx0OaTwOb8S0cngbePgH5PDZ8//MhL7HjGZ9PA74XjvfvgBUTkM8SfMFeGTdvUhxLfHDaA/Tg\nz6KuxN+TehTYGj6rQ9oVwI/i1v1w+J1uAz6U5Txuw9cBx36fsZZ2s4GHRvt9ZDmf/xF+dy/gC7BZ\nw/MZpkeUC9nMZ5h/W+w3GZd2Io9nsnIoI79PdTEhIhJxerJYRCTiFAhERCJOgUBEJOIUCEREIk6B\nQEQk4hQIJFLMrMrMPjlGmi9lKz8ik4Gaj0qkhH5bHnTOvWWUNK3OubKsZUpkguVNdAZEsuwm4LjQ\n5/xvgOOBCvz/wieA9wHFYflm59waM/tL4Gp8N8nPAJ90zvWZWSvwb8CfAAeA1c65xqzvkcg4qWpI\nouZafHfYy4CXgf8N40uB55xz1wIdzrllIQicAHwA3+HYMqAPWBO2VYrvQ2k58Avg+mzvjEg66IpA\nouw3wI9D514/c84lejPVSuBk4DehS6RiBjv66mewk7L/BEZ0UCgyFeiKQCLL+ZeUvBPYBfyHmV2W\nIJkBt4crhGXOueOdczck22SGsiqSUQoEEjUt+Ff/YWbHAA3OuR/ie3qMvdO5J1wlgO/Ya5WZ1YV1\nqsN64P9/VoXxDwK/ykL+RdJOVUMSKc65JjN7Iry8vBRoM7MeoBWIXRHcArxgZs+G+wR/h38zVQ6+\n18pPATuBNmCxmW3Cv03vA9neH5F0UPNRkSOkZqZytFDVkIhIxOmKQEQk4nRFICIScQoEIiIRp0Ag\nIhJxCgQiIhGnQCAiEnH/H+k3EVtiaGJ+AAAAAElFTkSuQmCC\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7fd082247198>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7fd082126630>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nESilVID7f/7YFJ+blHWwAAAAAElFTkSuQmCC\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7fd082126b00>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noUMmlSunJdAAAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7fd08206ef98>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7fd081e8d860>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"analysis.plot_all('soil_output/Spread_barabasi*', attributes=['id'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2017-07-03T14:43:49.238790Z",
|
||
"start_time": "2017-07-03T16:43:20.939175+02:00"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xt8XFW99/HPL8kkadK0aXqB3qQFK5ce2gIFQVQ4Vq5y\n8XgKVnu4iaCC4v2A8CjoI0c8eA5HFC8oCGjlYhHh8OARDlBBbtIiVMrFUiiStrShl9A0aa6/54+9\nJplMZpJJOzNJu7/v12tesy9r9l6zM1m/vddae21zd0REJL5KhjoDIiIytBQIRERiToFARCTmFAhE\nRGJOgUBEJOYUCEREYk6BQLqZ2U1m9u3dZT+FZmaXmtnPhzofqczsbDP70zDIx2oz++BQ50Nyo0Aw\nBMzsvWb2uJk1mtkmM3vMzA4d6nzJ4Lj7v7n7J4c6H7sTM5tnZi+ZWbOZPWxme/WTdlpI0xw+o8Cz\ngxQIiszMRgH3Aj8A6oDJwDeB1kFux8xsWP/9zKxsGOShdKjzMBjD4ZgNpFB5NLNxwG+BrxP9bywF\nbu/nI7cCfwHGApcBi81sfCHytrsb1gXJbupdAO5+q7t3unuLu9/v7svDZf1jZvaDcLXwkpnNS37Q\nzJaY2ZVm9hjQDOxtZqPN7AYzW2dma8zs28nCz8z2MbOHzGyjmb1lZovMrDZleweZ2TNmttXMbgcq\nc/kCZnaSmT1rZlvClc2slHWrzexiM1sObDOzsoH2Y2bnmdkr4eroHjObFJabmV1jZhvC8VhuZv8w\nQN5uMrMfm9l9ZrYN+EczqzCz75nZ381svZn9xMxGhPRHm1m9mX057GedmZ0T1h0a0pelbP+fzezZ\nMH2Fmf0qh+N1ppm9Hv4OX0+tNgnbWGxmvzKzt4GzzewwM3siHN91ZvZDMytP2Z6b2UVm9mr4u16d\nflIQvu9mM3vNzE7IIY9LzOw7ZvbncKzvNrO6sG5a2Oe5ZvZ34KGw/BQzWxHyucTM9k/b7KFm9kLI\nxy/MbKDf10eAFe7+G3ffDlwBzDaz/TLk913AwcDl4X/oTuCvwD8P9F2lLwWC4vsb0GlmN5vZCWY2\nJm39u4FXgXHA5cBvk/+QwRnA+UAN8DpwM9ABvBM4CDgWSFZXGPAdYBKwPzCV6J+LULD8Dvgl0dnX\nb8jhn8jMDgZuBD5FdCb2U+AeM6tISfYx4ENALdFvLOt+zOwDIY+nAxPDd7otrD4WeD9R8KwFPgps\nHCiPwMeBK4mO0Z+A74ZtzCE6TpOBb6Sk3xMYHZafC1xnZmPc/emwv2NS0v5L+C45MbMDgB8BC8P3\nS+4n1anA4vAdFwGdwBeJfgNHAPOAC9I+80/AXKLC8FTgEynr3g28HD7/78ANZmY5ZPfMsJ1JRL+p\na9PWH0X0OzouFMS3Al8AxgP3Af+dGrDCdz4O2Ifo+P+fAfY/E3guOePu24BVYXmmtK+6+9aUZc9l\nSSsDcXe9ivwi+me6Cagn+oe7B9gDOBtYC1hK2j8DZ4TpJcC3UtbtQVSlNCJl2ceAh7Ps98PAX8L0\n+zPs63Hg2wPk/cfA/01b9jJwVJheDXwiZV2/+wFuAP49Zd1IoB2YBnyAKHAeDpTkeGxvAm5JmTdg\nG7BPyrIjgNfC9NFAC1CWsn4DcHiYvhhYFKbriK7EJob5K4BfDZCfbwC3psxXAW3AB1O28cgA2/gC\ncFfKvAPHp8xfADwYps8GXknbnwN7DrCPJcBVKfMHhHyWhr+FA3unrP86cEfKfAmwBjg65Xfw6ZT1\nJwKrBsjDDal5CMseA87OkPYM4Mm0ZVcCN+3s/2ccX8O+PnJ35O4vEv3DEi57fwX8F/AHYI2HX3Xw\nOtEZWtIbKdN7AQlgXcoJX0kyjZlNIDqrex/R2XEJsDmkm5RlXwPZCzjLzD6Xsqy8nzwOtJ9JwDPJ\nGXdvMrONwGR3f8jMfghcB7zDzO4CvuLubw+Qx9T9jycqDJelHCMjKuCSNrp7R8p8M1FAguhv86KZ\njSS6annU3dcNsP9Uk1Lz4+7N4ftly2+y2uM/ic74q4AyYFk/n0n/jbyZtj9Svk9/0reZILqqyLR+\nEil/R3fvMrM36H21018eM2kCRqUtGwVs3cm0MgBVDQ0xd3+J6Cw2Wfc9Oe0y/h1EZ9TdH0mZfoPo\nimCcu9eG1yh3T14efyekn+Xuo4iqNZLbXpdlXwN5A7gyZX+17l7l7rdmyeNA+1lLFFwAMLNqoiqn\nNQDufq27H0J0yf8u4Ks55DF1/28RnfHPTMnvaHfPpWDE3dcATxBVxZzBIKqFgnXAlORMaJsY209+\nIbrqegmYEf5ul9Lzd0uamjKd/hvZUenbbCc6fpnymf53s/D5NTuRxxXA7JRtVhNVK63IknZvM6tJ\nWTY7S1oZgAJBkZnZfqFhckqYn0pUnfNkSDIBuMjMEmZ2GlE10n2ZthXOTO8H/sPMRplZiUUNxEeF\nJDVEZ05bzGwyvQvRJ4iqpS6yqEH3I8BhOXyFnwGfNrN3W6TazD6U9g+ZaqD9/Bo4x8zmhHaGfwOe\ncvfVobH23WaWIKre2U5Uf54zd+8Keb4mXCFhZpPN7LhBbOYW4F+BA4G7BrN/orr/k83sPaH+/Jv0\nLdTT1QBvA03hivEzGdJ81czGhN/P5+m/d02u/sXMDjCzKuBbwGJ3z3a87wA+ZFF3zwTwZaKTksdT\n0lxoZlNCG9elOeTxLuAfLGqQrySqVlseTpZ6cfe/Ac8Cl5tZpZn9EzALuDP3rytJCgTFt5WoMe8p\ni3q1PAk8T/SPBPAUMIPoTOxKYL6799dAeiZR1cwLRNU+i4kaJSEqdA4GGoH/R9Q1DwB3byPqpXF2\n+NxHU9dn4+5LgfOAH4bPvRK2kS19v/tx9weJ6pvvJDp73gdYEFaPIirENxNVLWwEvjdQHjO4OOTz\nydAz53+BfQfx+buIzn7v8qgBM2fuvgL4HFED+Dqiv/8G+u8u/BWiBu+tRN8/UwF6N1F10bNEf9sb\nBpOvLH5JdHX6JlHProuyJXT3l4muMH9A9Fs9GTg5/L2Tfk10ovJqePV7E6G7NxB1JLiS6G/+bnp+\nC1jU2+snKR9ZQFR9thm4iuh/pSGH7ylprHfVrQwlMzsb+KS7v3eo8yK9mdkq4FPu/r87uZ2RwBai\nap/XdnAbHj7/ys7kJW2bS4gavofVndJSHLoiEBmAmf0zUf34Qzv4+ZPNrCrUeX+PqL/76vzlUGTn\nKBBIHxaNodOU4fX7oc4bQLiJKVP+FhZgX0uIGm8vDO0NmdIszJKfZMPlqUQNpWuJqv0W+BBcimfJ\nY5OZva+IeRjWv624UtWQiEjM6YpARCTmhsUNZePGjfNp06YNdTZERHYpy5Yte8vdd3qgvWERCKZN\nm8bSpUuHOhsiIrsUM8tlNIABqWpIRCTmFAhERGJOgUBEJOYUCEREYk6BQEQk5nIKBBY9Wu+vFj2e\ncGlYVmdmD5jZyvA+Jiw3M7vWokcPLrfoiVYiIjJMDeaK4B/dfY67zw3zlxA9FWkG8GCYBziB6Db6\nGUSPVPxxvjIrIiL5tzP3EZxK9Jg/iJ6bu4RouN9TiR4V6ETD/taa2cR+n+q09U144joor4bykeG9\nOsP8SChN7ESWRUQkXa6BwIH7w/C3P3X364E9koW7u69LPvSD6FF1qY+oqw/LegUCMzuf6IqBQyaW\nwB8uzS0npRWZA0blaBgxJnpV1fVM93rVQaIyx68sIhIPuQaCI919bSjsHzCzPk8MSpHp6Ut9RrYL\nweR6gLlzD3EufhDatoVXU5bpbOua4K310LIZmjdBV3s/33hEWnCo7ZmuqEm7EhnZz5XJsLgpW0Rk\np+VUmrn72vC+waIHiB8GrE9W+ZjZRKKnLkF0BZD6rNIpDPisUgsFcu0gs58xs9DeHAWEls1ZXpug\nZUs0venVnuUd23PfT7Yrk17BJEMAyRhkwrKy8p3//iIigzRgIAgP0yhx961h+lii55neA5xF9Ii4\ns4genUdY/lkzu43oUXON/bYP5JtZT+FaO3Xg9Kk62we4+sh2pRKmW5tg21u913W05L7/kgRUjYXR\nU1JeU6PvkZweMSb6jiIieZLLFcEewF0WFT5lwK/d/X/M7GngDjM7F/g7cFpIfx9wItEzYpuBc/Ke\n60IpTeTvyiSpqzPH4NIUBZLmt6CxHtY/D3/7n75XKYnqfgLFFKiZpCsLERmUAQOBu78KzM6wfCMw\nL8NyBy7MS+52ByWlUDkqeg2WOzRvhC1/j4JD9yvMv7kctqU/q9tC9VS29o0s1VUVI3vPJ9tNElW6\nAhHZzanFczgzg+px0Wtylvvy2lvg7bW9g8X2xr6N7E3r065MtkLmJy/2VlqeufdVaiN7pt5a5SMV\nQER2EQoEu7rECBi7T/QaDPeo2ilTdVVrE2zf0tMLK7WhfcsbsG551ODe3px9+yVl2bvwpvfWSn1V\njIISjXwiUkwKBHFlFgWRxIjoimNHtG/vCRjJV3fgSOmZ1bI5umpZ/0K0vK2pn3yVQGVt//eCZOr+\nW1mrACKygxQIZMclKiGxJ9TsObjPdbT1DSDpr2RAadoADS9HQaW1sZ+NWuarjMSIqHqrtDzqDJDz\ndPK9om97SmKEqr1kt6JAIMVXVg4jJ0SvwejsiNo/Wvq5RyQZQJo3wcZV0NEKna1R1+DOtmi+7/2N\ng2S5NcAnG+ETVVmCTUXuwam8Oup4IFIACgSy6ygtg+qx0WtndHVGQaGzrSdApE53pASOzlZoa87h\nvpJtUdffLa/3bmvxzvx8dyuF6vEhgO4RXhN63mv27JlWQ70MkgKBxE9JKZSE9pFCco+CSds26OrI\nEGQyBKGM063RlVDT+qiqbOubsH4FbNsQbTddoqp3kBi5Z/SeqOp9tVFWkaEqLNN0RTSdqIy6Jstu\nR4FApFDMosK2rKIw2+/qCu0o69NeG3qmG/4Grz0atcnkw4gxULcP1O0dvcamTFfV5WcfUnQKBCK7\nqpKSnqqyPQ7oP21HWzTcSc5XIRmmW5tg8+pofK6/Pwl//Q292lsqa3uCQp8gMVbVVcOYAoFIHJSV\n53/okY7WnsCw6dWocX7Tq1D/NKz4be8bFitGQ930aDiUPl2AM9yQWOhqO+lFgUBEdkxZBYzfN3ql\n62iLGs7Tg0TD36JqqgGHi6/MECxqe+5az6VNI1u34LKK6ApFwaabAoGI5F9ZOYybEb0ySQ4X3+dG\nxEyvLbDptZ5uw4MZLr4/FaPSemHt0Xu+JrxXjd3tu+4qEIhI8aUOFz96yuA+29UVXU2kt2F0tOXW\n5tGxPRrMsWkDNL0Zvb+5PHpvfTtDXktSuu6GbrrVY6OrlkHdpJg2XVY5bJ5FokAgIruWkhIoKVBv\nrLbmvj2vek2vhw0vRM8d6WzN335LErmPFJw6nycKBCIiSeVVUaN23fSB07qHmxNbd6AXVns0cnB7\nM7Ruzf7Mkrfr+64rAAUCEZEdYRbd7V5aBlQXZ59dXVE34LZtUQD55jvzslkFAhGRXUVJSU/V0GDH\n6upvs3nbkoiI7JIUCEREYk6BQEQk5hQIRERiToFARCTmFAhERGJOgUBEJOYUCEREYk6BQEQk5hQI\nRERiToFARCTmFAhERGJOgUBEJOYUCEREYi7nQGBmpWb2FzO7N8xPN7OnzGylmd1uZuVheUWYfyWs\nn1aYrIuISD4M5org88CLKfPfBa5x9xnAZuDcsPxcYLO7vxO4JqQTEZFhKqdAYGZTgA8BPw/zBnwA\nWByS3Ax8OEyfGuYJ6+eF9CIiMgzlekXwX8C/Al1hfiywxd07wnw9MDlMTwbeAAjrG0P6XszsfDNb\namZLGxoadjD7IiKyswYMBGZ2ErDB3ZelLs6Q1HNY17PA/Xp3n+vuc8ePH59TZkVEJP9yeWbxkcAp\nZnYiUAmMIrpCqDWzsnDWPwVYG9LXA1OBejMrA0YDm/KecxERyYsBrwjc/WvuPsXdpwELgIfcfSHw\nMDA/JDsLuDtM3xPmCesfcvc+VwQiIjI87Mx9BBcDXzKzV4jaAG4Iy28AxoblXwIu2bksiohIIeVS\nNdTN3ZcAS8L0q8BhGdJsB07LQ95ERKQIdGexiEjMKRCIiMScAoGISMwpEIiIxJwCgYhIzCkQiIjE\nnAKBiEjMKRCIiMScAoGISMwpEIiIxJwCgYhIzCkQiIjEnAKBiEjMKRCIiMScAoGISMwpEIiIxJwC\ngYhIzCkQiIjEnAKBiEjMKRCIiMScAoGISMwpEIiIxJwCgYhIzCkQiIjEnAKBiEjMKRCIiMScAoGI\nSMwpEIiIxJwCgYhIzJUNdQZEJH7a29upr69n+/btQ52VXUJlZSVTpkwhkUgUZPsDBgIzqwQeASpC\n+sXufrmZTQduA+qAZ4Az3L3NzCqAW4BDgI3AR919dUFyLyK7pPr6empqapg2bRpmNtTZGdbcnY0b\nN1JfX8/06dMLso9cqoZagQ+4+2xgDnC8mR0OfBe4xt1nAJuBc0P6c4HN7v5O4JqQTkSk2/bt2xk7\ndqyCQA7MjLFjxxb06mnAQOCRpjCbCC8HPgAsDstvBj4cpk8N84T180x/bRFJo2Ihd4U+Vjk1FptZ\nqZk9C2wAHgBWAVvcvSMkqQcmh+nJwBsAYX0jMDbDNs83s6VmtrShoWHnvoWIiOywnAKBu3e6+xxg\nCnAYsH+mZOE9U+jyPgvcr3f3ue4+d/z48bnmV0SkIEaOHDnUWRgyg+o+6u5bgCXA4UCtmSUbm6cA\na8N0PTAVIKwfDWzKR2ZFRCT/BgwEZjbezGrD9Ajgg8CLwMPA/JDsLODuMH1PmCesf8jd+1wRiIgU\n0sUXX8yPfvSj7vkrrriCb37zm8ybN4+DDz6YAw88kLvvvrvP55YsWcJJJ53UPf/Zz36Wm266CYBl\ny5Zx1FFHccghh3Dcccexbt26gn+PYsjlimAi8LCZLQeeBh5w93uBi4EvmdkrRG0AN4T0NwBjw/Iv\nAZfkP9siIv1bsGABt99+e/f8HXfcwTnnnMNdd93FM888w8MPP8yXv/xlcj1PbW9v53Of+xyLFy9m\n2bJlfOITn+Cyyy4rVPaLasD7CNx9OXBQhuWvErUXpC/fDpyWl9yJiOyggw46iA0bNrB27VoaGhoY\nM2YMEydO5Itf/CKPPPIIJSUlrFmzhvXr17PnnnsOuL2XX36Z559/nmOOOQaAzs5OJk6cWOivURS6\ns1hEdlvz589n8eLFvPnmmyxYsIBFixbR0NDAsmXLSCQSTJs2rU///LKyMrq6urrnk+vdnZkzZ/LE\nE08U9TsUg8YaEpHd1oIFC7jttttYvHgx8+fPp7GxkQkTJpBIJHj44Yd5/fXX+3xmr7324oUXXqC1\ntZXGxkYefPBBAPbdd18aGhq6A0F7ezsrVqwo6vcpFF0RiMhua+bMmWzdupXJkyczceJEFi5cyMkn\nn8zcuXOZM2cO++23X5/PTJ06ldNPP51Zs2YxY8YMDjooqhkvLy9n8eLFXHTRRTQ2NtLR0cEXvvAF\nZs6cWeyvlXc2HDr0zJ0715cuXTrU2RCRInnxxRfZf/9MtyNJNpmOmZktc/e5O7ttVQ2JiMScAoGI\nSMwpEIiIxJwCgYhIzCkQiIjEnAKBiEjMKRCISCy95z3vGTDNo48+ysyZM5kzZw4tLS2D2v7vfvc7\nXnjhhUHnayiGw1YgEJFYevzxxwdMs2jRIr7yla/w7LPPMmLEiEFtf0cDwVBQIBCRWEqeeS9ZsoSj\njz6a+fPns99++7Fw4ULcnZ///OfccccdfOtb32LhwoUAXH311Rx66KHMmjWLyy+/vHtbt9xyC7Nm\nzWL27NmcccYZPP7449xzzz189atfZc6cOaxatYpVq1Zx/PHHc8ghh/C+972Pl156CYDXXnuNI444\ngkMPPZSvf/3rxT8QaIgJERli3/zvFbyw9u28bvOASaO4/OTch374y1/+wooVK5g0aRJHHnkkjz32\nGJ/85Cf505/+xEknncT8+fO5//77WblyJX/+859xd0455RQeeeQRxo4dy5VXXsljjz3GuHHj2LRp\nE3V1dZxyyindnwWYN28eP/nJT5gxYwZPPfUUF1xwAQ899BCf//zn+cxnPsOZZ57Jddddl9fjkCsF\nAhGJvcMOO4wpU6YAMGfOHFavXs173/veXmnuv/9+7r///u6xh5qamli5ciXPPfcc8+fPZ9y4cQDU\n1dX12X5TUxOPP/44p53WM0J/a2srAI899hh33nknAGeccQYXX3xx/r/gABQIRGRIDebMvVAqKiq6\np0tLS+no6OiTxt352te+xqc+9aley6+99lrMMj2qvUdXVxe1tbU8++yzGdcP9PlCUxuBiEgOjjvu\nOG688UaampoAWLNmDRs2bGDevHnccccdbNy4EYBNm6JHtNfU1LB161YARo0axfTp0/nNb34DREHl\nueeeA+DII4/ktttuA6LG6aGgQCAikoNjjz2Wj3/84xxxxBEceOCBzJ8/n61btzJz5kwuu+wyjjrq\nKGbPns2XvvQlIHoWwtVXX81BBx3EqlWrWLRoETfccAOzZ89m5syZ3c9L/v73v891113HoYceSmNj\n45B8Nw1DLSJFp2GoB0/DUIuISMEoEIiIxJwCgYhIzCkQiIjEnAKBiEjMKRCIiMScAoGIyE5YvXo1\nv/71r3fos0Mx5HQmCgQiIjuhv0CQaaiK4UiBQERiafXq1ey///6cd955zJw5k2OPPZaWlpasw0Wf\nffbZLF68uPvzybP5Sy65hEcffZQ5c+ZwzTXXcNNNN3Haaadx8sknc+yxx9LU1MS8efM4+OCDOfDA\nA7vvKB5ONOiciAyt318Cb/41v9vc80A44aoBk61cuZJbb72Vn/3sZ5x++unceeed/OIXv8g4XHQ2\nV111Fd/73ve49957Abjpppt44oknWL58OXV1dXR0dHDXXXcxatQo3nrrLQ4//HBOOeWUIR9oLpUC\ngYjE1vTp05kzZw4AhxxyCKtXr846XPRgHHPMMd3DUbs7l156KY888gglJSWsWbOG9evXs+eee+bn\nS+SBAoGIDK0cztwLJX346fXr12cdLrqsrIyuri4gKtzb2tqybre6urp7etGiRTQ0NLBs2TISiQTT\npk1j+/btefwWO2/ANgIzm2pmD5vZi2a2wsw+H5bXmdkDZrYyvI8Jy83MrjWzV8xsuZkdXOgvISKS\nD/0NFz1t2jSWLVsGwN133017ezvQe7jpTBobG5kwYQKJRIKHH36Y119/vcDfYvByaSzuAL7s7vsD\nhwMXmtkBwCXAg+4+A3gwzAOcAMwIr/OBH+c91yIiBZJtuOjzzjuPP/7xjxx22GE89dRT3Wf9s2bN\noqysjNmzZ3PNNdf02d7ChQtZunQpc+fOZdGiRey3335F/T65GPQw1GZ2N/DD8Dra3deZ2URgibvv\na2Y/DdO3hvQvJ9Nl26aGoRaJFw1DPXjDZhhqM5sGHAQ8BeyRLNzD+4SQbDLwRsrH6sOy9G2db2ZL\nzWxpQ0PD4HMuIiJ5kXMgMLORwJ3AF9z97f6SZljW57LD3a9397nuPnf8+PG5ZkNERPIsp0BgZgmi\nILDI3X8bFq8PVUKE9w1heT0wNeXjU4C1+cmuiOwuhsPTEXcVhT5WufQaMuAG4EV3/8+UVfcAZ4Xp\ns4C7U5afGXoPHQ409tc+ICLxU1lZycaNGxUMcuDubNy4kcrKyoLtI5f7CI4EzgD+ambJzrWXAlcB\nd5jZucDfgeQdGPcBJwKvAM3AOXnNsYjs8qZMmUJ9fT1qH8xNZWUlU6ZMKdj2BwwE7v4nMtf7A8zL\nkN6BC3cyXyKyG0skEkyfPn2osyGBBp0TEYk5BQIRkZhTIBARiTkFAhGRmFMgEBGJOQUCEZGYUyAQ\nEYk5BQIRkZhTIBARiTkFAhGRmFMgEBGJOQUCEZGYUyAQEYk5BQIRkZhTIBARiTkFAhGRmFMgEBGJ\nOQUCEZGYUyAQEYk5BQIRkZhTIBARiTkFAhGRmFMgEBGJOQUCEZGYUyAQEYk5BQIRkZhTIBARiTkF\nAhGRmFMgEBGJOQUCEZGYGzAQmNmNZrbBzJ5PWVZnZg+Y2crwPiYsNzO71sxeMbPlZnZwITMvIiI7\nL5crgpuA49OWXQI86O4zgAfDPMAJwIzwOh/4cX6yKSIihTJgIHD3R4BNaYtPBW4O0zcDH05ZfotH\nngRqzWxivjIrIiL5t6NtBHu4+zqA8D4hLJ8MvJGSrj4s68PMzjezpWa2tKGhYQezISIiOyvfjcWW\nYZlnSuju17v7XHefO378+DxnQ0REcrWjgWB9ssonvG8Iy+uBqSnppgBrdzx7IiJSaDsaCO4BzgrT\nZwF3pyw/M/QeOhxoTFYhiYjI8FQ2UAIzuxU4GhhnZvXA5cBVwB1mdi7wd+C0kPw+4ETgFaAZOKcA\neRYRkTwaMBC4+8eyrJqXIa0DF+5spkREpHh0Z7GISMwpEIiIxJwCgYhIzCkQiIjEnAKBiEjMKRCI\niMScAoGISMwpEIiIxJwCgYhIzCkQiIjEnAKBiEjMKRCIiMScAoGIyC4oGuMzPwYcfVRERAanq8tp\n7+qivdNp7+iivbOL1vDe3ulhvpNtrZ00t3WwrbWTbeG9OfW9rZPm1g62tXXQ3NbJttbe7/miQCAi\nksLdaWxpp2Fra/Rqit43JOe3trJpWxutHZ3dhXp7ZxdtHT2FfEfXjp+tm0F1eRlV5aVUV4T38jLq\nqsuZOqaq1/KLv5Of76xAICK7jY5wxt3W2dVdQLd39J5v7ehi07a2PoV7Q1Mrb4Xpts6uPtuuKCth\nwqgKxo+sYFJtJRWJUspLS0iUGonSEhKlJZSXpc0n15elzkfLKhKljKwopaq8LCr4K6ICvzJRglmm\nx7/3dXGejpsCgYgMqe3tnTS2tLOluZ0tzW1saWmnsbmdLS1t0bIwv7k5mt/e3plSsEdVL8n5wZ6I\nm8HY6nLG11QyvqaCd44fyfiaCsbXVDAhvCdfNRVlORfQuxoFAhEpiI7OLtZsaWH1xmZWv7WN1Ru3\nsW7L9u4CPln4t7Rnr+suKzFqq8qprUpQOyLBxNGVVFeUhbPvnjPv6Gw7zJelzYdlqfN11eVMqKmg\nrrqcslI/uBuFAAAL8ElEQVT1mVEgEJEd1t7ZRf3mFlZv3Mbqt7bx+sbm7un6zS296sqrykuZXDuC\nMVXlTK2r4sARCcZUlzN6RCIU9FGBn5wfU1VOVXnpbnsWPpwoEIjElLvT0eUZ69Gjxs+UhtDOLppb\nO3l9UzOvb9zG6o3Re/3mFjpTCvvq8lKmjatm5qTRfGjWRPYaW820sdVMG1fF+JEVKtSHKQUCkV1c\nW0cXG7e1suHt3r1cosbQ7TRsbeWtpjZa2jtDod/ToLojairKmDaumgMnj+aU2ZNCYV/FtHHVjK0u\nV2G/C1IgECmyri7v3djZ3fUwZT6lwG7v7OrpztjUu4Bv2NrK5ub2jPsZU5Xobug86B21VJWX5VSP\nXl5WktbrpaeXy9QxI6hTYb/bUSAQCdy9d7/wUEC3tHWm3NgT3ejT1NpBc7gJKNONPtvaetZvb+/s\n7mPe1tnVqyplsFK7MO49biTvnj62p2fLyNDbZVQFY6srKC9TI6jkRoFAdjldXc7W7R29uhduCV0L\nt4Ruh41heWtHVAi3pdzhmTzz7j4r38mqkspESa9+4FXlpYysKGNCTQXVFWVUhv7m6X3MK1LOvBNp\nZ+KJUovOxsOymspoeyN34y6MMnQUCGTIdHZ56ELYt+/45uZ2GsPynr7kIV1LO/0Ns1JTUcboqqjn\nyYhEKYnSEqrKS3aoy2FFaUmvAr77Ts/wXlVeRmmJCmbZtSkQyE7rCHXYfc/MewrvzeFmodQbh97e\n3tHvdmsqyxhT1dOl8B11VYwJ/clHV5VTm+x2WJWI+pqPSDBqRIKE+oWLDIoCgQBRf/Dm7oGvOroL\n7M2phXfKjUDJuzwbm9vZ2pq9QDcj6hceCu+66nL2HldNbVVK//HQh3x0KOTHVJVTU1mmG31EikSB\nYDfQ2eVs2LqdtVtaWLNlO2+3tGcZwTBtZMPWnkbNgerHS4zus+7aqgQTaip514SaUHiX9zkzH51S\noJeo6kRkWFMg2AVsa+0IhXwLa7dsZ82W5vDewtotLbzZuD3raIfJeuzq7sGtShkVbtVPXd49+FV4\n73WnZ1WCkeUq0EV2VwoEReTubG/v6j4Lb2rt6HPGvrGptfvMfu2WFtY2trAlrZ94aYmx56hKJteO\nYO5eY5g8ZgSTasNr9AjGVCWorihjRKJUhbeIDEiBIIuuLqelvXd/8NSqlOi9/+qWPn3M2zr67e2S\nVFNZxuRQsB+y15hQyFd2L9tjVKV6qohI3hQkEJjZ8cD3gVLg5+5+VSH2M5Dk8LabU3qyNPbqe54y\n39ze6wlBg3n6T2mJdT88orqip2vhnqMqqaqIqmP6q4apThmTvLY6wajKRAGPiohIb3kPBGZWClwH\nHAPUA0+b2T3u/kIun890Jt7c1tn7Ts60M/FtrR28vb2919C2W1ra2N6evQE0UWqMHtEzvO2k2spQ\ngPdfUGfqU15RlvuDJEREhptCXBEcBrzi7q8CmNltwKlA1kDw8ptbmfvt/x30mXiJ0V0YR10Uo+Ft\nZ01J9O6emKFXi4a3FRGJFCIQTAbeSJmvB96dnsjMzgfOBxg1aW+OOWCPqAolWZWSsUpFZ+IiIvlW\niECQqWTu00Tq7tcD1wPMnTvXv/ORAwuQFRERGUghbt2sB6amzE8B1hZgPyIikgeFCARPAzPMbLqZ\nlQMLgHsKsB8REcmDvFcNuXuHmX0W+ANR99Eb3X1FvvcjIiL5UZD7CNz9PuC+QmxbRETyS8M7iojE\nnAKBiEjMKRCIiMScAoGISMyZ5zIcZqEzYbYVeHmo85GDccBbQ52JHCif+bMr5BGUz3zbVfK5r7vX\n7OxGhssw1C+7+9yhzsRAzGyp8pk/u0I+d4U8gvKZb7tSPvOxHVUNiYjEnAKBiEjMDZdAcP1QZyBH\nymd+7Qr53BXyCMpnvsUqn8OisVhERIbOcLkiEBGRIaJAICISc0UNBGZ2vJm9bGavmNklGdZXmNnt\nYf1TZjatmPkLeZhqZg+b2YtmtsLMPp8hzdFm1mhmz4bXN4qdz5CP1Wb215CHPt3ILHJtOJ7Lzezg\nIudv35Rj9KyZvW1mX0hLM2TH0sxuNLMNZvZ8yrI6M3vAzFaG9zFZPntWSLPSzM4qch6vNrOXwt/0\nLjOrzfLZfn8fRcjnFWa2JuVve2KWz/ZbLhQhn7en5HG1mT2b5bPFPJ4Zy6GC/T7dvSgvoiGpVwF7\nA+XAc8ABaWkuAH4SphcAtxcrfyl5mAgcHKZrgL9lyOfRwL3FzluGvK4GxvWz/kTg90RPjTsceGoI\n81oKvAnsNVyOJfB+4GDg+ZRl/w5cEqYvAb6b4XN1wKvhfUyYHlPEPB4LlIXp72bKYy6/jyLk8wrg\nKzn8LvotFwqdz7T1/wF8Yxgcz4zlUKF+n8W8Iuh+qL27twHJh9qnOhW4OUwvBuZZkR9K7O7r3P2Z\nML0VeJHoOcy7olOBWzzyJFBrZhOHKC/zgFXu/voQ7b8Pd38E2JS2OPU3eDPw4QwfPQ54wN03uftm\n4AHg+GLl0d3vd/eOMPsk0VMAh1SWY5mLXMqFvOkvn6GsOR24tVD7z1U/5VBBfp/FDASZHmqfXsB2\npwk/9EZgbFFyl0GomjoIeCrD6iPM7Dkz+72ZzSxqxno4cL+ZLTOz8zOsz+WYF8sCsv+DDYdjmbSH\nu6+D6J8RmJAhzXA6rp8guurLZKDfRzF8NlRh3ZilGmM4Hcv3AevdfWWW9UNyPNPKoYL8PosZCHJ5\nqH1OD74vBjMbCdwJfMHd305b/QxRFcds4AfA74qdv+BIdz8YOAG40Mzen7Z+WBxPix5Zegrwmwyr\nh8uxHIzhclwvAzqARVmSDPT7KLQfA/sAc4B1RNUu6YbFsQw+Rv9XA0U/ngOUQ1k/lmFZv8e0mIEg\nl4fad6cxszJgNDt2ublTzCxBdPAXuftv09e7+9vu3hSm7wMSZjauyNnE3deG9w3AXUSX2alyOebF\ncALwjLuvT18xXI5livXJ6rPwviFDmiE/rqEB8CRgoYeK4XQ5/D4Kyt3Xu3unu3cBP8uy/yE/ltBd\n3nwEuD1bmmIfzyzlUEF+n8UMBLk81P4eINnCPR94KNuPvFBCPeENwIvu/p9Z0uyZbLsws8OIjuPG\n4uUSzKzazGqS00QNiM+nJbsHONMihwONycvKIst6pjUcjmWa1N/gWcDdGdL8ATjWzMaE6o5jw7Ki\nMLPjgYuBU9y9OUuaXH4fBZXWHvVPWfafS7lQDB8EXnL3+kwri308+ymHCvP7LEYLeEpr9olErd+r\ngMvCsm8R/aABKomqD14B/gzsXcz8hTy8l+gyajnwbHidCHwa+HRI81lgBVEPhyeB9wxBPvcO+38u\n5CV5PFPzacB14Xj/FZg7BPmsIirYR6csGxbHkig4rQPaic6iziVqk3oQWBne60LaucDPUz77ifA7\nfQU4p8h5fIWoDjj5+0z2tJsE3Nff76PI+fxl+N0tJyrAJqbnM8z3KReKmc+w/KbkbzIl7VAez2zl\nUEF+nxpiQkQk5nRnsYhIzCkQiIjEnAKBiEjMKRCIiMScAoGISMwpEEismFmtmV0wQJpLi5UfkeFA\n3UclVsK4Lfe6+z/0k6bJ3UcWLVMiQ6xsqDMgUmRXAfuEMeefBvYFRhH9L3wG+BAwIqxf4e4Lzexf\ngIuIhkl+CrjA3TvNrAn4KfCPwGZggbs3FP0biewkVQ1J3FxCNBz2HOAl4A9hejbwrLtfArS4+5wQ\nBPYHPko04NgcoBNYGLZVTTSG0sHAH4HLi/1lRPJBVwQSZ08DN4bBvX7n7pmeTDUPOAR4OgyJNIKe\ngb666Bmk7FdAnwEKRXYFuiKQ2PLoISXvB9YAvzSzMzMkM+DmcIUwx933dfcrsm2yQFkVKSgFAomb\nrUSP/sPM9gI2uPvPiEZ6TD7TuT1cJUA0sNd8M5sQPlMXPgfR/8/8MP1x4E9FyL9I3qlqSGLF3Tea\n2WPh4eXVwDYzaweagOQVwfXAcjN7JrQT/B+iJ1OVEI1aeSHwOrANmGlmy4iepvfRYn8fkXxQ91GR\nHaRuprK7UNWQiEjM6YpARCTmdEUgIhJzCgQiIjGnQCAiEnMKBCIiMadAICISc/8f28O0H33XKZ8A\nAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7fd081c96978>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7fd081c99f60>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7fd081b4e7f0>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhPdhAd1fv7EAAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7fd0822186a0>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIjLK/X+WBgkuYDfsgAAAAABJRU5ErkJggg==\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7fd0814fb898>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"analysis.plot_all('soil_output/Spread_erdos*', attributes=['id'])"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.6.1"
|
||
},
|
||
"toc": {
|
||
"colors": {
|
||
"hover_highlight": "#DAA520",
|
||
"navigate_num": "#000000",
|
||
"navigate_text": "#333333",
|
||
"running_highlight": "#FF0000",
|
||
"selected_highlight": "#FFD700",
|
||
"sidebar_border": "#EEEEEE",
|
||
"wrapper_background": "#FFFFFF"
|
||
},
|
||
"moveMenuLeft": true,
|
||
"nav_menu": {
|
||
"height": "31px",
|
||
"width": "252px"
|
||
},
|
||
"navigate_menu": true,
|
||
"number_sections": true,
|
||
"sideBar": true,
|
||
"threshold": 4,
|
||
"toc_cell": false,
|
||
"toc_section_display": "block",
|
||
"toc_window_display": true,
|
||
"widenNotebook": false
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|